37 Catboost Jobs

We are looking for a highly experienced Data Scientist to lead data-driven initiatives and deliver scalable machine learning solutions to complex business problems. The ideal candidate will combine technical depth in ML/AI with strong business acumen, driving impact through advanced analytics, feature engineering, and model deployment. Key Responsibilities Partner with stakeholders to understand business objectives and translate them into data science solutions. Lead the end-to-end lifecycle of data science projects — from problem framing, data exploration, and feature engineering to model development, validation, and production deployment. Apply advanced ML algorithms (XGBoost, LightGBM, CatBoost, etc.) to solve classification, regression, and forecasting problems. Conduct clustering and unsupervised learning (KMeans, DBSCAN, hierarchical clustering) for segmentation, anomaly detection, and pattern recognition. Optimize models for accuracy, interpretability, and scalability in production environments. Build reproducible, well-documented data science workflows and collaborate closely with engineering teams on deployment. Mentor and guide junior data scientists, setting best practices in experimentation, evaluation, and MLOps. Communicate findings and recommendations to technical and non-technical stakeholders through compelling visualizations and narratives. Required Skills & Qualifications 8–12 years of professional experience in Data Science, Machine Learning, or Applied AI. Strong expertise in ML algorithms (XGBoost, LightGBM, CatBoost, ensemble methods). Solid experience in feature engineering, data preprocessing, and statistical modeling. Proficiency in clustering (KMeans, DBSCAN, etc.) and unsupervised learning techniques. Hands-on programming skills in Python (pandas, scikit-learn, NumPy, matplotlib, seaborn). Familiarity with MLOps tools, cloud platforms (AWS, Azure, GCP), and version control. Strong problem-solving skills with the ability to link analytics outcomes to business impact. Excellent communication and leadership skills with experience leading cross-functional projects.

As an AI/ML Developer, you will be responsible for utilizing programming languages such as Python for AI/ML development. Your proficiency in libraries like NumPy, Pandas for data manipulation, Matplotlib, Seaborn, Plotly for data visualization, and Scikit-learn for classical ML algorithms will be crucial. Familiarity with R, Java, or C++ is a plus, especially for performance-critical applications. Your role will involve building models using Machine Learning & Deep Learning Frameworks such as TensorFlow and Keras for deep learning, PyTorch for research-grade and production-ready models, and XGBoost, LightGBM, or CatBoost for gradient boosting. Understanding model training, validation, hyperparameter tuning, and evaluation metrics like ROC-AUC, F1-score, precision/recall will be essential. In the field of Natural Language Processing (NLP), you will work with text preprocessing techniques like tokenization, stemming, lemmatization, vectorization techniques such as TF-IDF, Word2Vec, GloVe, and Transformer-based models like BERT, GPT, T5 using Hugging Face Transformers. Experience with text classification, named entity recognition (NER), question answering, or chatbot development will be required. For Computer Vision (CV), your experience with image classification, object detection, segmentation, and libraries like OpenCV, Pillow, and Albumentations will be utilized. Proficiency in pretrained models (e.g., ResNet, YOLO, EfficientNet) and transfer learning is expected. You will also handle Data Engineering & Pipelines by building and managing data ingestion and preprocessing pipelines using tools like Apache Airflow, Luigi, Pandas, Dask. Experience with structured (CSV, SQL) and unstructured (text, images, audio) data will be beneficial. Furthermore, your role will involve Model Deployment & MLOps where you will deploy models as REST APIs using Flask, FastAPI, or Django, batch jobs, or real-time inference services. Familiarity with Docker for containerization, Kubernetes for orchestration, and MLflow, Kubeflow, or SageMaker for model tracking and lifecycle management will be necessary. In addition, your hands-on experience with at least one cloud provider such as AWS (S3, EC2, SageMaker, Lambda), Google Cloud (Vertex AI, BigQuery, Cloud Functions), or Azure (Machine Learning Studio, Blob Storage) will be required. Understanding cloud storage, compute services, and cost optimization is essential. Your proficiency in SQL for querying relational databases (e.g., PostgreSQL, MySQL), NoSQL databases (e.g., MongoDB, Cassandra), and familiarity with big data tools like Apache Spark, Hadoop, or Databricks will be valuable. Experience with Git and platforms like GitHub, GitLab, or Bitbucket will be essential for Version Control & Collaboration. Familiarity with Agile/Scrum methodologies and tools like JIRA, Trello, or Asana will also be beneficial. Moreover, you will be responsible for writing unit tests and integration tests for ML code and using tools like pytest, unittest, and debuggers to ensure the quality of the code. This position is Full-time and Permanent with benefits including Provident Fund and Work from home option. The work location is in person.,

Position Overview Here at ShyftLabs, we are searching for an experienced Data Scientist who can derive performance improvement and cost efficiency in our product through a deep understanding of the ML and infra system, and provide a data driven insight and scientific solution ShyftLabs is a growing data product company that was founded in early 2020 and works primarily with Fortune 500 companies. We deliver digital solutions built to help accelerate the growth of businesses in various industries, by focusing on creating value through innovation. Job Responsibilities: Research, design, and develop innovative generative AI models and applications Collaborate with cross-functional teams to identify opportunities for AI-driven solutions Train and fine-tune AI models on large datasets to achieve optimal performance Optimize AI models for deployment in production environments Stay up-to-date with the latest advancements in AI and machine learning Collaborate with data scientists and engineers to ensure data quality and accessibility Design, implement, and optimize machine learning algorithms for tasks like classification, prediction, and clustering Develop and maintain robust AI infrastructure Document technical designs, decisions, and processes, and communicate progress and results to stakeholders Work with cross-functional teams to integrate AI/ML models into production-level applications Basic Qualifications: Master's degree in a quantitative discipline or equivalent 5+ years minimum professional experience Distinctive problem-solving skills, good at articulating product questions, pulling data from large datasets, and using statistics to arrive at a recommendation Excellent verbal and written communication skills, with the ability to present information and analysis results effectively Ability to build positive relationships within ShyftLabs and with our stakeholders, and work effectively with cross-functional partners in a global company Must have a deep understanding of ML algorithms, ranging from classical methods (e.g., regression, random forests, k-means clustering) to advanced techniques such as gradient boosting (XGBoost, LightGBM, CatBoost), neural networks, and transformer-based architectures (e.g., sentence transformers, BERT variants) End-to-End Deployment: Proven experience building, training, and deploying ML models from scratch into production environments, including model lifecycle management (versioning, monitoring, and retraining) Scalability & Performance: Hands-on experience operationalizing models at scale, optimizing for performance, reliability, and cost efficiency in real-world production systems Programming: experience with Python or other scripting languages and database language (e.g., SQL) or data manipulation (e.g., Pandas) We are proud to offer a competitive salary alongside a strong insurance package. We pride ourselves on the growth of our employees, offering extensive learning and development resources.

Greetings From Right Move !!!!!!! Please see below the Job Description . If you find this interesting and in line with your career aspirations, kindly revert to this email with your confirmation & updated CV. Experience – 7+ Years. Role – AI/ML Engineer. Job Type - Permanent. Mode – Work from Office. Company Location - Pune, Bhosari. Skill Set - AI/ML, MLOPS, Cloud, Python, Traditional AI,ML Algorithms, TensorFlow, PyTorch, Keras, Scikit-learn, XGBoost, LightGBM, and CatBoost. About Company: The AI COE (Centre of Excellence) where their central team of experts dedicated to accelerating the adoption of artificial intelligence and machine learning across the organization. The AI COE is responsible for making a business impact across the group of companies adding value chain—including Manufacturing, Supply Chain, R&D, Sales, Marketing, and Security—and is also responsible for AI governance. We partner with various business units to identify high-impact use cases, develop cutting-edge AI/ML solutions, and build and deploy them through Applied AI and innovation to deliver a strong ROI. Our focus is on fostering innovation, establishing best practices, and enabling the digital transformation of our operations, particularly within all Automotive sectors. Job Description: We are seeking a highly experienced and senior Machine Learning Engineer to join our AI COE in Pune. You will be responsible for the hands-on design, development, and deployment of advanced machine learning and deep learning models for our Manufacturing, R&D, Design, Sales & Marketing, and "Plants AI" initiatives. You will leverage your deep technical expertise to solve complex challenges in areas such as predictive maintenance, supply chain optimization, customer analytics, and process automation. A critical part of this role will be your proven ability to optimize and manage the entire lifecycle of ML models in production, especially for real-time performance on edge devices. Required Skills ● Advanced degrees (M.E, M.Tech) in Engineering, Computer Science, Statistics, or related fields with specialization in machine learning or AI are highly preferred. A Bachelor's of Engineering (B.E) is the minimum requirement. ● A minimum of 7 years of demonstrable, hands-on execution experience covering the full AI/ML lifecycle, including ML algorithm development, model development, deployment, and optimization in production environments. ● Direct experience working within a production or manufacturing environment, with a deep understanding of the unique challenges, operational constraints, and industrial settings. ● Proven ability in technical project management, with demonstrated success leading cross-functional teams and managing projects from inception to deployment. ● Strong programming skills in Python and C++, with Python being strongly preferred. Experience in software development for engineering applications and familiarity with version control systems like Git or TFS are also required. ● Deep familiarity with a wide range of machine learning frameworks and libraries, such as TensorFlow, PyTorch, Keras, Scikit-learn, XGBoost, LightGBM, and CatBoost. Required Qualifications & Skills: ● Core Machine Learning & AI: Deep proficiency in a wide range of ML and AI technologies. ● ML Algorithms: Extensive hands-on experience with supervised learning (e.g., SVM, Random Forests), gradient boosting (XGBoost, LightGBM, CatBoost), and unsupervised learning (e.g., K-Means, PCA, Isolation Forest). ● Deep Learning Architectures: In-depth knowledge of convolutional neural networks (CNNs), recurrent neural networks (RNNs), LSTMs, Transformers, and Generative Adversarial Networks (GANs). ● LLM and Generative AI: Strong familiarity with the architecture and application of Large Language Models (LLMs), including hands-on experience with popular open-source LLMs (e.g., Llama, Mistral, Falcon, Gemma families). Experience with fine-tuning, prompt engineering, and Retrieval-Augmented Generation (RAG) is highly desirable. ● AI for Design: Experience or strong interest in applying AI techniques to design processes, such as AI for CAS (Computer-Aided Styling) or CAD (Computer-Aided Design), is a significant plus. Cloud ML & Edge Computing: ● Cloud ML Engineering: Extensive hands-on experience with the ML services of major cloud providers, such as AWS SageMaker, Azure Machine Learning, Google Cloud AI Platform (Vertex AI), and OCI AI Services. Proven ability to train, optimize, and deploy large-scale machine learning models in a cloud environment. ● MLOps, LLMOps, & AIOps: Solid understanding and practical, hands-on experience with MLOps principles for building robust CI/CD pipelines, model/data versioning, monitoring, and governance. Knowledge of AI governance principles and security frameworks is essential. ● Edge Optimization: Demonstrated expertise in optimizing ML models for edge devices. This includes hands-on experience with quantization (INT8, FP16), pruning, knowledge distillation, and using runtimes like NVIDIA TensorRT, Qualcomm SNPE, Intel's OpenVINO, TensorFlow Lite, and ONNX Runtime. *We are looking for candidates who can join us within 30 days* If you find this interesting and inline with your career aspiration, please share your CV on aishwarya@rightmoveconsultants.com or call on 8799997177 Thanks & Regards, Aishwarya D

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title And Summary Lead Data Scientist Job Description For Lead Data Scientist Use the Mastercard standardized job description template to design a simple and engaging vision of the job opportunity you have available. Remember to: Follow the guidelines in each section to write the content for your position. Copy and paste it into the Workday Job Description Summary field. Who is Mastercard? Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments, and businesses realize their greatest potential. Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all. Overview Finicity, a Mastercard company, is leading the Open Banking Initiative to increase the Financial Health of consumers and businesses. The Data Science and Analytics team is looking for a Data Scientist II. The Data Science team works on Intelligent Decisioning; Financial Certainty; Attribute, Feature, and Entity Resolution; Verification Solutions and much more. Join our team to make an impact across all sectors of the economy by consistently innovating and problem-solving. The ideal candidate is passionate about leveraging data to provide high quality customer solutions. Also, the candidate is a strong technical leader who is extremely motivated, intellectually curious, analytical, and possesses an entrepreneurial mindset. Role Develops machine-learning models to monitor open banking transactions in order to glean insights from the data and create data science algorithms to detect data anomaly observed in fraudulent transactions. Manipulates large data sets and applies various technical and statistical analytical techniques (e.g., OLS, multinomial logistic regression, LDA, clustering, segmentation) to draw insights from large datasets. Apply various Machine learning (i.e. SVM, Radom Forest, XGBoost, LightGBM, CATBoost etc), Deep learning techniques (i.e. LSTM, RNN, Transformer etc.) to solve analytical problem statement. Design and implement machine learning models for a number of financial applications including but not limited to: Transaction Classification, Temporal Analysis, Risk modeling from structured and unstructured data. Measure, validate, implement, monitor and improve performance of both internal and external facing machine learning models. Propose creative solutions to existing challenges that are new to the company, the financial industry and to data science. Present technical problems and findings to business leaders internally and to clients succinctly and clearly. Leverage best practices in machine learning and data engineering to develop scalable solutions. Identify areas where resources fall short of needs and provide thoughtful and sustainable solutions to benefit the team Be a strong, confident, and excellent writer and speaker, able to communicate your analysis, vision and roadmap effectively to a wide variety of stakeholders Test current cutting-edge AI technologies to enhance data science modeling work. All About You: 5-7 years in data science/ machine learning model development and deployments Exposure to financial transactional structured and unstructured data, transaction classification, risk evaluation and credit risk modeling is a plus. A strong understanding of NLP, Statistical Modeling, Visualization and advanced Data Science techniques/methods. AI experience is a plus. Gain insights from text, including non-language tokens and use the thought process of annotations in text analysis. Solve problems that are new to the company, the financial industry and to data science SQL / Database experience is preferred. Strong Python programming background/experience. Experience with Kubernetes, Containers, Docker, REST APIs, Event Streams or other delivery mechanisms. Familiarity with relevant technologies (e.g. Tensorflow, Sklearn, Pandas, etc.). Familiarity with Databricks Platform. Strong desire to collaborate and ability to come up with creative solutions. Additional Finance and FinTech experience preferred. Bachelor’s or Master’s Degree in Computer Science, Information Technology, Engineering, Mathematics, Statistics. Corporate Security Responsibility Every Person Working For, Or On Behalf Of, Mastercard Is Responsible For Information Security. All Activities Involving Access To Mastercard Assets, Information, And Networks Comes With An Inherent Risk To The Organization And Therefore, It Is Expected That The Successful Candidate For This Position Must: Abide by Mastercard’s security policies and practices; Ensure the confidentiality and integrity of the information being accessed; Report any suspected information security violation or breach, and Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines. NOTE: Candidates go through a thorough screening and interview process. Corporate Security Responsibility All Activities Involving Access To Mastercard Assets, Information, And Networks Comes With An Inherent Risk To The Organization And, Therefore, It Is Expected That Every Person Working For, Or On Behalf Of, Mastercard Is Responsible For Information Security And Must: Abide by Mastercard’s security policies and practices; Ensure the confidentiality and integrity of the information being accessed; Report any suspected information security violation or breach, and Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.

Management Level : 07- I&F Decision Sci Practitioner Manager Location : Mumbai Must-have skills : Risk Analytics, Model Development, Validation, and Auditing, Performance Evaluation, Monitoring, Governance, Statistical Techniques: Linear Regression, Logistic Regression, GLM, GBM, XGBoost, CatBoost, Neural Networks, Programming Languages: SAS, R, Python, Spark, Scala, Tools: Tableau, QlikView, PowerBI, SAS VA, Regulatory Knowledge: Basel/CCAR/DFAST/CECL/IFRS9, Risk Reporting and Dashboard Solutions Good to have skills : Advanced Data Science Techniques, AML, Operational Risk Modelling, Cloud Platform Experience (AWS/Azure/GCP), Machine Learning Interpretability and Bias Algorithms Job Summary We are seeking a highly skilled I&F Decision Sci Practitioner Manager to join the Accenture Strategy & Consulting team in the Global Network – Data & AI practice. You will be responsible for leading risk model development, validation, and auditing activities, ensuring performance evaluation, monitoring, governance, and documentation. This role also provides opportunities to work with top financial clients globally, utilizing cutting-edge technologies to drive business capabilities and foster innovation. Roles & Responsibilities: Engagement Execution Lead the team in the development, validation, governance, strategy, transformation, implementation, and end-to-end delivery of risk solutions for clients. Manage workstreams for large and small projects, overseeing the quality of deliverables for junior team members. Develop and frame Proof of Concept for key clients where applicable. Practice Enablement Mentor, guide, and counsel analysts and consultants. Support the development of the practice by driving innovations and initiatives. Support efforts of sales team to identify and win potential opportunities by assisting with RFPs, RFI. Assist in designing POVs, GTM collateral. Professional & Technical Skills: 7-12 years of relevant Risk Analytics experience at one or more Financial Services firms or Professional Services / Risk Advisory with significant exposure to: Credit Risk: PD/LGD/EAD Models, CCAR/DFAST Loss Forecasting, Revenue Forecasting Models, IFRS9/CECL Loss Forecasting across Retail and Commercial portfolios. Credit Acquisition/Behavior: Modeling, Credit Policies, Limit Management, Acquisition Frauds, Collections Agent Matching/Channel Allocations across Retail and Commercial portfolios. Regulatory Capital and Economic Capital Models Liquidity Risk: Liquidity Models, Stress Testing Models, Basel Liquidity Reporting Standards Anti-Money Laundering (AML): AML Scenarios/Alerts, Network Analysis Operational Risk: AMA Modeling, Operational Risk Reporting Modeling Techniques: Linear Regression, Logistic Regression, GLM, GBM, XGBoost, CatBoost, Neural Networks, Time Series (ARMA/ARIMA), ML Interpretability and Bias Algorithms Programming Languages & Tools: SAS, R, Python, Spark, Scala, Tableau, QlikView, PowerBI, SAS VA Strong understanding of Risk functions and their application in client discussions and project implementation. Additional Information: Master’s Degree in a quantitative discipline (mathematics, statistics, economics, financial engineering, operations research) or MBA from top-tier universities Industry Certifications: FRM, PRM, CFA preferred Excellent Communication and Interpersonal Skills About Our Company | Accenture

Company Description NovaIA offers an AI-powered voice assistant tool designed to support human agents in real time. Particularly tailored for real estate agencies, the assistant can make calls, follow up with leads, filter prospects, and schedule appointments. Key features include real-time agent support and appointment management automation. The assistant listens in on conversations, providing live guidance, data, or suggestions, and seamlessly handles follow-ups and meeting setups through voice interactions. We’re looking for a versatile and hands-on Data Scientist who can bridge the gap between traditional machine learning and conversational AI. You'll work on predictive modeling tasks (e.g., user behavior, conversion forecasting) and also contribute to intelligent voicebots that respond in real-time. This role offers a balance of experimentation, productionization, and product collaboration—ideal for someone who thrives at the intersection of models and applications. Job Title: Data Scientist – Predictive Modeling & Conversational AI Location: Gurgram (On Site) Experience: 3+ years Working Hours: Full time Key Responsibilities Design, build, and evaluate machine learning models for classification, regression, clustering, and ranking use cases Analyze large datasets to extract insights, train predictive models, and improve decision-making Lead and support analytics use cases such as behavior prediction, engagement scoring, and feature engineering Work on NLP/NLU tasks including intent recognition, entity extraction, summarization, and semantic similarity Contribute to conversational AI logic such as dynamic routing, fallback response logic, and session personalization Design evaluation frameworks to assess real-time model performance in voice-based interfaces Collaborate with engineering teams to deploy models in production environments and monitor model health Core Skills ML/DS toolkits: scikit-learn, XGBoost, LightGBM, CatBoost, PyCaret Data wrangling: pandas, NumPy, Polars, SQL (PostgreSQL, BigQuery) NLP frameworks: HuggingFace Transformers, spaCy, NLTK, fastText ML ops understanding: model versioning, performance monitoring, feature store design Working with structured + unstructured data (voice/text/logs) Comfortable writing modular, reusable code in Python or notebooks with best practices Preferred / Bonus Skills LLM integration: prompt engineering, fine-tuning open-source models (e.g., Mistral, LLaMA) Time series forecasting (Prophet, ARIMA, or ML-based) Recommender systems or ranking algorithms (collaborative filtering, hybrid models) Familiarity with RAG pipelines, embeddings, vector search, and hybrid retrieval Experience using experiment tracking tools (MLflow, Weights & Biases, DVC) Exposure to speech/audio data analytics General Qualities We Value Comfort working in fast-paced, ambiguous environments Startup or early product-building experience with cross-functional teams Strong problem-solving ability and interest in building user-facing intelligence Demonstrated portfolio of work (e.g., GitHub, notebooks, blog posts, Kaggle) Curiosity, autonomy, and eagerness to contribute across the stack when needed Note: If Question is Not Applicable: Write NA

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title And Summary Lead Data Scientist Job Description For Lead Data Scientist Use the Mastercard standardized job description template to design a simple and engaging vision of the job opportunity you have available. Remember to: Follow the guidelines in each section to write the content for your position. Copy and paste it into the Workday Job Description Summary field. Who is Mastercard? Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments, and businesses realize their greatest potential. Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all. Overview Finicity, a Mastercard company, is leading the Open Banking Initiative to increase the Financial Health of consumers and businesses. The Data Science and Analytics team is looking for a Data Scientist II. The Data Science team works on Intelligent Decisioning; Financial Certainty; Attribute, Feature, and Entity Resolution; Verification Solutions and much more. Join our team to make an impact across all sectors of the economy by consistently innovating and problem-solving. The ideal candidate is passionate about leveraging data to provide high quality customer solutions. Also, the candidate is a strong technical leader who is extremely motivated, intellectually curious, analytical, and possesses an entrepreneurial mindset. Role Develops machine-learning models to monitor open banking transactions in order to glean insights from the data and create data science algorithms to detect data anomaly observed in fraudulent transactions. Manipulates large data sets and applies various technical and statistical analytical techniques (e.g., OLS, multinomial logistic regression, LDA, clustering, segmentation) to draw insights from large datasets. Apply various Machine learning (i.e. SVM, Radom Forest, XGBoost, LightGBM, CATBoost etc), Deep learning techniques (i.e. LSTM, RNN, Transformer etc.) to solve analytical problem statement. Design and implement machine learning models for a number of financial applications including but not limited to: Transaction Classification, Temporal Analysis, Risk modeling from structured and unstructured data. Measure, validate, implement, monitor and improve performance of both internal and external facing machine learning models. Propose creative solutions to existing challenges that are new to the company, the financial industry and to data science. Present technical problems and findings to business leaders internally and to clients succinctly and clearly. Leverage best practices in machine learning and data engineering to develop scalable solutions. Identify areas where resources fall short of needs and provide thoughtful and sustainable solutions to benefit the team Be a strong, confident, and excellent writer and speaker, able to communicate your analysis, vision and roadmap effectively to a wide variety of stakeholders Test current cutting-edge AI technologies to enhance data science modeling work. All About You: 5-7 years in data science/ machine learning model development and deployments Exposure to financial transactional structured and unstructured data, transaction classification, risk evaluation and credit risk modeling is a plus. A strong understanding of NLP, Statistical Modeling, Visualization and advanced Data Science techniques/methods. AI experience is a plus. Gain insights from text, including non-language tokens and use the thought process of annotations in text analysis. Solve problems that are new to the company, the financial industry and to data science SQL / Database experience is preferred. Strong Python programming background/experience. Experience with Kubernetes, Containers, Docker, REST APIs, Event Streams or other delivery mechanisms. Familiarity with relevant technologies (e.g. Tensorflow, Sklearn, Pandas, etc.). Familiarity with Databricks Platform. Strong desire to collaborate and ability to come up with creative solutions. Additional Finance and FinTech experience preferred. Bachelor’s or Master’s Degree in Computer Science, Information Technology, Engineering, Mathematics, Statistics. Corporate Security Responsibility Every Person Working For, Or On Behalf Of, Mastercard Is Responsible For Information Security. All Activities Involving Access To Mastercard Assets, Information, And Networks Comes With An Inherent Risk To The Organization And Therefore, It Is Expected That The Successful Candidate For This Position Must: Abide by Mastercard’s security policies and practices; Ensure the confidentiality and integrity of the information being accessed; Report any suspected information security violation or breach, and Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines. NOTE: Candidates go through a thorough screening and interview process. Corporate Security Responsibility All Activities Involving Access To Mastercard Assets, Information, And Networks Comes With An Inherent Risk To The Organization And, Therefore, It Is Expected That Every Person Working For, Or On Behalf Of, Mastercard Is Responsible For Information Security And Must: Abide by Mastercard’s security policies and practices; Ensure the confidentiality and integrity of the information being accessed; Report any suspected information security violation or breach, and Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.

Job Title: AI/ML Ops Engineer Location: Ahmedabad - Onsite Duration: 2-4 years experience (Candidates below 2 year and above 4 years - PLEASE DO NOT APPLY) About the Role We are seeking an experienced AI/ML Ops Engineer to join our team and drive the development, deployment, and operationalization of machine learning and large language model (LLM) systems. You will be responsible for building scalable ML pipelines, enabling intelligent retrieval-augmented generation (RAG) capabilities, and deploying services that power intelligent enterprise applications. Key Responsibilities Develop and maintain machine learning models to forecast user behavior using structured time-series data. Build and optimize end-to-end regression pipelines using advanced libraries such as CatBoost, XGBoost, and LightGBM. Design and implement RAG (Retrieval-Augmented Generation) pipelines for enterprise chatbot systems utilizing tools like LangChain, LLM Router, or custom-built orchestrators. Work with vector databases for semantic document retrieval and reranking. Integrate external APIs into LLM workflows to enable tool/function calling capabilities. Package and deploy ML services using tools such as Docker, FastAPI, or Flask. Collaborate with cross-functional teams to ensure reliable CI/CD deployment and version control practices. Core Technologies & Tools Languages: Python (primary), Bash, SQL ML Libraries: scikit-learn, CatBoost, XGBoost, LightGBM, PyTorch, TensorFlow LLM & RAG Tools: LangChain, Hugging Face Transformers, LlamaIndex, LLM Router Vector Stores: FAISS, Weaviate, Chroma, Pinecone Deployment & APIs: Docker, FastAPI, Flask, Postman Infrastructure & Version Control: Git, GitHub, CI/CD pipeline Preferred Qualifications Proven experience in ML Ops, AI infrastructure, or productionizing ML models. Strong understanding of large-scale ML system design and deployment strategies. Experience working with vector databases and LLM-based applications in production.

Job Title: AI/ML Ops Engineer Location: Ahmedabad - Onsite Duration: 2-4 years experience (Candidates below 2 year and above 4 years - PLEASE DO NOT APPLY) About the Role We are seeking an experienced AI/ML Ops Engineer to join our team and drive the development, deployment, and operationalization of machine learning and large language model (LLM) systems. You will be responsible for building scalable ML pipelines, enabling intelligent retrieval-augmented generation (RAG) capabilities, and deploying services that power intelligent enterprise applications. Key Responsibilities Develop and maintain machine learning models to forecast user behavior using structured time-series data. Build and optimize end-to-end regression pipelines using advanced libraries such as CatBoost, XGBoost, and LightGBM. Design and implement RAG (Retrieval-Augmented Generation) pipelines for enterprise chatbot systems utilizing tools like LangChain, LLM Router, or custom-built orchestrators. Work with vector databases for semantic document retrieval and reranking. Integrate external APIs into LLM workflows to enable tool/function calling capabilities. Package and deploy ML services using tools such as Docker, FastAPI, or Flask. Collaborate with cross-functional teams to ensure reliable CI/CD deployment and version control practices. Core Technologies & Tools Languages: Python (primary), Bash, SQL ML Libraries: scikit-learn, CatBoost, XGBoost, LightGBM, PyTorch, TensorFlow LLM & RAG Tools: LangChain, Hugging Face Transformers, LlamaIndex, LLM Router Vector Stores: FAISS, Weaviate, Chroma, Pinecone Deployment & APIs: Docker, FastAPI, Flask, Postman Infrastructure & Version Control: Git, GitHub, CI/CD pipeline Preferred Qualifications Proven experience in ML Ops, AI infrastructure, or productionizing ML models. Strong understanding of large-scale ML system design and deployment strategies. Experience working with vector databases and LLM-based applications in production.

Job Title: AI/ML Ops Engineer Location: Ahmedabad - Onsite Duration: 2-4 years experinece About the Role We are seeking an experienced AI/ML Ops Engineer to join our team and drive the development, deployment, and operationalization of machine learning and large language model (LLM) systems. You will be responsible for building scalable ML pipelines, enabling intelligent retrieval-augmented generation (RAG) capabilities, and deploying services that power intelligent enterprise applications. Key Responsibilities Develop and maintain machine learning models to forecast user behavior using structured time-series data. Build and optimize end-to-end regression pipelines using advanced libraries such as CatBoost , XGBoost , and LightGBM . Design and implement RAG (Retrieval-Augmented Generation) pipelines for enterprise chatbot systems utilizing tools like LangChain , LLM Router , or custom-built orchestrators. Work with vector databases for semantic document retrieval and reranking. Integrate external APIs into LLM workflows to enable tool/function calling capabilities. Package and deploy ML services using tools such as Docker , FastAPI , or Flask . Collaborate with cross-functional teams to ensure reliable CI/CD deployment and version control practices. Core Technologies & Tools Languages: Python (primary), Bash, SQL ML Libraries: scikit-learn, CatBoost, XGBoost, LightGBM, PyTorch, TensorFlow LLM & RAG Tools: LangChain, Hugging Face Transformers, LlamaIndex, LLM Router Vector Stores: FAISS, Weaviate, Chroma, Pinecone Deployment & APIs: Docker, FastAPI, Flask, Postman Infrastructure & Version Control: Git, GitHub, CI/CD pipelines Preferred Qualifications Proven experience in ML Ops, AI infrastructure, or productionizing ML models. Strong understanding of large-scale ML system design and deployment strategies. Experience working with vector databases and LLM-based applications in production. Job Type: Full-time Pay: Up to ₹75,229.87 per month Benefits: Provident Fund Experience: AI/ML: 3 years (Preferred) ML OPs: 3 years (Preferred) AWS: 1 year (Preferred) Python: 3 years (Preferred) Work Location: In person

Job Title : AI/ML Ops Engineer Location: Ahmedabad - Onsite Duration: 2-4 years experience (Candidates below 2 year and above 4 years - PLEASE DO NOT APPLY) About the Role We are seeking an experienced AI/ML Ops Engineer to join our team and drive the development, deployment, and operationalization of machine learning and large language model (LLM) systems. You will be responsible for building scalable ML pipelines, enabling intelligent retrieval-augmented generation (RAG) capabilities, and deploying services that power intelligent enterprise applications. Key Responsibilities Develop and maintain machine learning models to forecast user behavior using structured time-series data. Build and optimize end-to-end regression pipelines using advanced libraries such as CatBoost, XGBoost, and LightGBM. Design and implement RAG (Retrieval-Augmented Generation) pipelines for enterprise chatbot systems utilizing tools like LangChain, LLM Router, or custom-built orchestrators. Work with vector databases for semantic document retrieval and reranking. Integrate external APIs into LLM workflows to enable tool/function calling capabilities. Package and deploy ML services using tools such as Docker, FastAPI, or Flask. Collaborate with cross-functional teams to ensure reliable CI/CD deployment and version control practices. Core Technologies & Tools Languages: Python (primary), Bash, SQL ML Libraries: scikit-learn, CatBoost, XGBoost, LightGBM, PyTorch, TensorFlow LLM & RAG Tools: LangChain, Hugging Face Transformers, LlamaIndex, LLM Router Vector Stores: FAISS, Weaviate, Chroma, Pinecone Deployment & APIs: Docker, FastAPI, Flask, Postman Infrastructure & Version Control: Git, GitHub, CI/CD pipeline Preferred Qualifications Proven experience in ML Ops, AI infrastructure, or productionizing ML models. Strong understanding of large-scale ML system design and deployment strategies. Experience working with vector databases and LLM-based applications in production.

Job Title: AI/ML Ops Engineer Location: Ahmedabad - Onsite Duration: 2-4 years experience (Ca ndidates below 2 year - PLEASE DO NOT APPLY) About the Role We are seeking an experienced AI/ML Ops Engineer to join our team and drive the development, deployment, and operationalization of machine learning and large language model (LLM) systems. You will be responsible for building scalable ML pipelines, enabling intelligent retrieval-augmented generation (RAG) capabilities, and deploying services that power intelligent enterprise applications. Key Responsibilities Develop and maintain machine learning models to forecast user behavior using structured time-series data. Build and optimize end-to-end regression pipelines using advanced libraries such as CatBoost , XGBoost , and LightGBM . Design and implement RAG (Retrieval-Augmented Generation) pipelines for enterprise chatbot systems utilizing tools like LangChain , LLM Router , or custom-built orchestrators. Work with vector databases for semantic document retrieval and reranking. Integrate external APIs into LLM workflows to enable tool/function calling capabilities. Package and deploy ML services using tools such as Docker , FastAPI , or Flask . Collaborate with cross-functional teams to ensure reliable CI/CD deployment and version control practices. Core Technologies & Tools Languages: Python (primary), Bash, SQL ML Libraries: scikit-learn, CatBoost, XGBoost, LightGBM, PyTorch, TensorFlow LLM & RAG Tools: LangChain, Hugging Face Transformers, LlamaIndex, LLM Router Vector Stores: FAISS, Weaviate, Chroma, Pinecone Deployment & APIs: Docker, FastAPI, Flask, Postman Infrastructure & Version Control: Git, GitHub, CI/CD pipeline Preferred Qualifications Proven experience in ML Ops, AI infrastructure, or productionizing ML models. Strong understanding of large-scale ML system design and deployment strategies. Experience working with vector databases and LLM-based applications in production.

Job Title: AI/ML Ops Engineer Location: Ahmedabad - Onsite Duration: 2-4 years experinece About the Role We are seeking an experienced AI/ML Ops Engineer to join our team and drive the development, deployment, and operationalization of machine learning and large language model (LLM) systems. You will be responsible for building scalable ML pipelines, enabling intelligent retrieval-augmented generation (RAG) capabilities, and deploying services that power intelligent enterprise applications. Key Responsibilities Develop and maintain machine learning models to forecast user behavior using structured time-series data. Build and optimize end-to-end regression pipelines using advanced libraries such as CatBoost , XGBoost , and LightGBM . Design and implement RAG (Retrieval-Augmented Generation) pipelines for enterprise chatbot systems utilizing tools like LangChain , LLM Router , or custom-built orchestrators. Work with vector databases for semantic document retrieval and reranking. Integrate external APIs into LLM workflows to enable tool/function calling capabilities. Package and deploy ML services using tools such as Docker , FastAPI , or Flask . Collaborate with cross-functional teams to ensure reliable CI/CD deployment and version control practices. Core Technologies & Tools Languages: Python (primary), Bash, SQL ML Libraries: scikit-learn, CatBoost, XGBoost, LightGBM, PyTorch, TensorFlow LLM & RAG Tools: LangChain, Hugging Face Transformers, LlamaIndex, LLM Router Vector Stores: FAISS, Weaviate, Chroma, Pinecone Deployment & APIs: Docker, FastAPI, Flask, Postman Infrastructure & Version Control: Git, GitHub, CI/CD pipelines Preferred Qualifications Proven experience in ML Ops, AI infrastructure, or productionizing ML models. Strong understanding of large-scale ML system design and deployment strategies. Experience working with vector databases and LLM-based applications in production. Job Type: Full-time Pay: Up to ₹75,229.87 per month Benefits: Provident Fund Experience: AI/ML: 3 years (Preferred) ML OPs: 3 years (Preferred) AWS: 1 year (Preferred) Python: 3 years (Preferred) Work Location: In person

Company Overview Docusign brings agreements to life. Over 1.5 million customers and more than a billion people in over 180 countries use Docusign solutions to accelerate the process of doing business and simplify people’s lives. With intelligent agreement management, Docusign unleashes business-critical data that is trapped inside of documents. Until now, these were disconnected from business systems of record, costing businesses time, money, and opportunity. Using Docusign’s Intelligent Agreement Management platform, companies can create, commit, and manage agreements with solutions created by the #1 company in e-signature and contract lifecycle management (CLM). What you'll do You will play an important role in applying and implementing effective machine learning solutions, with a significant focus on Generative AI. You will work with product and engineering teams to contribute to data-driven product strategies, explore and implement GenAI applications, and deliver impactful insights. This positionis an individual contributor role reporting to the Senior Manager, Data Science. Responsibility Experiment with, apply, and implement DL/ML models, with a strong emphasis on Large Language Models (LLMs), Agentic Frameworks, and other Generative AI techniques to predict user behavior, enhance product features, and improve automation Utilize and adapt various GenAI techniques (e.g., prompt engineering, RAG, fine-tuning existing models) to derive actionable insights, generate content, or create novel user experiences Collaborate with product, engineering, and other teams (e.g., Sales, Marketing, Customer Success) to build Agentic system to run campaigns at-scale Conduct in-depth analysis of customer data, market trends, and user insights to inform the development and improvement of GenAI-powered solutions Partner with product teams to design, administer, and analyze the results of A/B and multivariate tests, particularly for GenAI-driven features Leverage data to develop actionable analytical insights & present findings, including the performance and potential of GenAI models, to stakeholders and team members Communicate models, frameworks (especially those related to GenAI), analysis, and insights effectively with stakeholders and business partners Stay updated on the latest advancements in Generative AI and propose their application to relevant business problems Complete assignments with a sense of urgency and purpose, identify and help resolve roadblocks, and collaborate with cross-functional team members on GenAI initiatives Job Designation Hybrid: Employee divides their time between in-office and remote work. Access to an office location is required. (Frequency: Minimum 2 days per week; may vary by team but will be weekly in-office expectation) Positions at Docusign are assigned a job designation of either In Office, Hybrid or Remote and are specific to the role/job. Preferred job designations are not guaranteed when changing positions within Docusign. Docusign reserves the right to change a position's job designation depending on business needs and as permitted by local law. What you bring Basic Bachelor's or Master's degree in Computer Science, Physics, Mathematics, Statistics, or a related field 3+ years of hands-on experience in building data science applications and machine learning pipelines, with demonstrable experience in Generative AI projects Experience with Python for research and software development purposes, including common GenAI libraries and frameworks Experience with or exposure to prompt engineering, and utilizing pre-trained LLMs (e.g., via APIs or open-source models) Experience with large datasets, distributed computing, and cloud computing platforms (e.g., AWS, Azure, GCP) Proficiency with relational databases (e.g., SQL) Experience in training, evaluating, and deploying machine learning models in production environments, with an interest in MLOps for GenAI Proven track record in contributing to ML/GenAI projects from ideation through to deployment and iteration Experience using machine learning and deep learning algorithms like CatBoost, XGBoost, LGBM, Feed Forward Networks for classification, regression, and clustering problems, and an understanding of how these can complement GenAI solutions Experience as a Data Scientist, ideally in the SaaS domain with some focus on AI-driven product features Preferred PhD in Statistics, Computer Science, or Engineering with specialization in machine learning, AI, or Statistics, with research or projects in Generative AI 5+ years of prior industry experience, with at least 1-2 years focused on GenAI applications Previous experience applying data science and GenAI techniques to customer success, product development, or user experience optimization Hands-on experience with fine-tuning LLMs or working with RAG methodologies Experience with or knowledge of experimentation platforms (like DataRobot) and other AI related ones (like CrewAI) Experience with or knowledge of the software development lifecycle/agile methodology, particularly in AI product development Experience with or knowledge of Github, JIRA/Confluence Contributions to open-source GenAI projects or a portfolio of GenAI related work Programming Languages like Python, SQL; familiarity with R Strong knowledge of common machine learning, deep learning, and statistics frameworks and concepts, with a specific understanding of Large Language Models (LLMs), transformer architectures, and their applications Ability to break down complex technical concepts (including GenAI) into simple terms to present to diverse, technical, and non-technical audiences Life at Docusign Working here Docusign is committed to building trust and making the world more agreeable for our employees, customers and the communities in which we live and work. You can count on us to listen, be honest, and try our best to do what’s right, every day. At Docusign, everything is equal. We each have a responsibility to ensure every team member has an equal opportunity to succeed, to be heard, to exchange ideas openly, to build lasting relationships, and to do the work of their life. Best of all, you will be able to feel deep pride in the work you do, because your contribution helps us make the world better than we found it. And for that, you’ll be loved by us, our customers, and the world in which we live. Accommodation Docusign is committed to providing reasonable accommodations for qualified individuals with disabilities in our job application procedures. If you need such an accommodation, or a religious accommodation, during the application process, please contact us at accommodations@docusign.com. If you experience any issues, concerns, or technical difficulties during the application process please get in touch with our Talent organization at taops@docusign.com for assistance. Applicant and Candidate Privacy Notice

As an AI/ML Manager at our Pune location, you will be responsible for leading the development of machine learning proof of concepts (PoCs) and demos using structured/tabular data for various use cases like forecasting, risk scoring, churn prediction, and optimization. Your role will involve collaborating with sales engineering teams to understand client requirements and presenting ML solutions during pre-sales calls and technical workshops. You will be expected to build ML workflows using tools such as SageMaker, Azure ML, or Databricks ML, managing training, tuning, evaluation, and model packaging. Applying supervised, unsupervised, and semi-supervised techniques like XGBoost, CatBoost, k-Means, PCA, and time-series models will be a key part of your responsibilities. Working closely with data engineering teams, you will define data ingestion, preprocessing, and feature engineering pipelines using Python, Spark, and cloud-native tools. Packaging and documenting ML assets for scalability and transition into delivery teams post-demo will be essential. Staying updated with the latest best practices in ML explainability, model performance monitoring, and MLOps practices is also expected. Participation in internal knowledge sharing, tooling evaluation, and continuous improvement of lab processes are additional aspects of this role. To qualify for this position, you should have at least 8+ years of experience in developing and deploying classical machine learning models in production or PoC environments. Strong hands-on experience with Python, pandas, scikit-learn, and ML libraries like XGBoost, CatBoost, LightGBM is required. Familiarity with cloud-based ML environments such as AWS SageMaker, Azure ML, or Databricks is preferred. A solid understanding of feature engineering, model tuning, cross-validation, and error analysis is necessary. Experience with unsupervised learning, clustering, anomaly detection, and dimensionality reduction techniques will be beneficial. You should be comfortable presenting models and insights to both technical and non-technical stakeholders during pre-sales engagements. Working knowledge of MLOps concepts, including model versioning, deployment automation, and drift detection, will be an advantage. If you are interested in this opportunity, please apply or share your resume at kanika.garg@austere.co.in.,

AI/ML Manager: Location – Pune Experience: 8+ years Notice period – Immediate to 30 days. Key Responsibilities: Lead the development of machine learning PoCs and demos using structured/tabular data for use cases such as forecasting, risk scoring, churn prediction, and optimization. Collaborate with sales engineering teams to understand client needs and present ML solutions during pre-sales calls and technical workshops. Build ML workflows using tools such as SageMaker, Azure ML, or Databricks ML and manage training, tuning, evaluation, and model packaging. Apply supervised, unsupervised, and semi-supervised techniques such as XGBoost, CatBoost, k-Means, PCA, time-series models, and more. Work with data engineering teams to define data ingestion, preprocessing, and feature engineering pipelines using Python, Spark, and cloud-native tools. Package and document ML assets so they can be scaled or transitioned into delivery teams post-demo. Stay current with best practices in ML explainability, model performance monitoring, and MLOps practices. Participate in internal knowledge sharing, tooling evaluation, and continuous improvement of lab processes. Qualifications: 8+ years of experience developing and deploying classical machine learning models in production or PoC environments. Strong hands-on experience with Python, pandas, scikit-learn, and ML libraries such as XGBoost, CatBoost, LightGBM, etc. Familiarity with cloud-based ML environments such as AWS SageMaker, Azure ML, or Databricks. Solid understanding of feature engineering, model tuning, cross-validation, and error analysis. Experience with unsupervised learning, clustering, anomaly detection, and dimensionality reduction techniques. Comfortable presenting models and insights to technical and non-technical stakeholders during pre-sales engagements. Working knowledge of MLOps concepts, including model versioning, deployment automation, and drift detection. Interested candidates shall apply or share resumes at kanika.garg@austere.co.in.

Job Description Design and develop machine learning models tailored to mechanical engineering challenges, including predictive modelling, simulation optimisation, and failure analysis. Utilise deep learning and other advanced ML techniques to improve the accuracy and efficiency of CAE simulations. Preprocess and analyse large datasets from CAE simulations, experimental tests, and manufacturing processes for modelling. Train, validate, and fine-tune machine learning models using real-world engineering data. Optimise models for performance, scalability, and robustness in production environments. Collaborate with CAE engineers to integrate ML models into existing simulation workflows (e.g., FEA, CFD, structural analysis). Automate repetitive simulation tasks and enable predictive analytics for design optimisation. Work closely with mechanical engineers, data scientists, and software developers to identify business challenges and develop data-driven solutions. Deploy machine learning models into production environments and monitor their performance. Maintain and update models to ensure reliability and continuous improvement. Stay abreast of the latest advancements in machine learning, AI, and CAE technologies. Apply innovative approaches to solve complex engineering problems. Requirements Bachelors or Master’s degree in Mechanical Engineering, Computer Science, or a related field Proven 2-3 years of experience in developing and deploying machine learning models, preferably in mechanical engineering or CAE domain Hands-on experience with CAE tools such as ANSYS, Abaqus, or similar FEA/CFD software Strong programming skills in Python, R, or Java Proficiency in machine learning frameworks (TensorFlow, PyTorch, scikit-learn) Experience with data preprocessing, feature engineering, and statistical analysis Solid understanding of mathematics, statistics, and problem-solving skills Excellent analytical thinking and ability to tackle complex engineering challenges Strong communication and teamwork skills to collaborate across disciplines Preferred: Experience with physics-informed machine learning and digital twin technologies Preferred: Familiarity with automation of CAE workflows and predictive modelling for product design Benefits Challenging job and a chance to team up with a young and dynamic professional group Chance to build yourself as WE grow. Remuneration that stays competitive and attractive to retain the best. Opportunity to join an organization experiencing year on year growth.

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together. Primary Responsibilities: Work closely with a lead-data scientist and help improve business processes leveraging data science tools & techniques in natural language and machine learning domain Understand business requirements and convert into analytical solution Analyze large amounts of information to discover trends and patterns Develop data science algorithms & generate actionable insights as per business needs and work closely with cross capability teams throughout solution development lifecycle from design to implementation & monitoring Understanding of Azure Cloud technologies such as Azure Data Factory, Azure SQL, Azure Blob Storage Azure data Bricks Manage day to day development tasks and stakeholder communication Document work appropriately for production support and transition readiness Acquire & enhance understanding of US Healthcare domain Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so Required Qualifications: Masters in Mathematics, Computer Science, Statistics or Machine Learning - any or equivalent 12+ months hands on experience of solving real world business problems leveraging data science tools Working knowledge of NLP skills (like Text Classification/Entity Recognition/Transfer Learning Concepts etc.) Sound knowledge of unstructured text data handling & manipulation; Programming: Python/SQL knowledge Good Theoretical knowledge of some or most Machine learning techniques like Random Forest, Gradient Boosting Machine, XGBoost, CATBoost etc. Programming: knows R/Python with PyTorch/TensorFlow, Knows how to leverage pre trained models (Via Transfer Learning) Proven excellent written and oral communication skills Proven excellent problem-solving & story-telling abilities with analytical mindset Proven excellent interpersonal and team skills Preferred Qualifications: Understanding & experience of SparkNLP framework Understanding of Responsible Use of AI, model fairness, bias assessment and its risk mitigations Exposure to RAG, LangChain, VectorDBs At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone-of every race, gender, sexuality, age, location and income-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together. Primary Responsibilities Work closely with a lead-data scientist and help improve business processes leveraging data science tools & techniques in natural language and machine learning domain Understand business requirements and convert into analytical solution Analyze large amounts of information to discover trends and patterns Develop data science algorithms & generate actionable insights as per business needs and work closely with cross capability teams throughout solution development lifecycle from design to implementation & monitoring Understanding of Azure Cloud technologies such as Azure Data Factory, Azure SQL, Azure Blob Storage Azure data Bricks Manage day to day development tasks and stakeholder communication Document work appropriately for production support and transition readiness Acquire & enhance understanding of US Healthcare domain Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so Required Qualifications Masters in Mathematics, Computer Science, Statistics or Machine Learning - any or equivalent 12+ months hands on experience of solving real world business problems leveraging data science tools Working knowledge of NLP skills (like Text Classification/Entity Recognition/Transfer Learning Concepts etc.) Sound knowledge of unstructured text data handling & manipulation; Programming: Python/SQL knowledge Good Theoretical knowledge of some or most Machine learning techniques like Random Forest, Gradient Boosting Machine, XGBoost, CATBoost etc. Programming: knows R/Python with PyTorch/TensorFlow, Knows how to leverage pre trained models (Via Transfer Learning) Proven excellent written and oral communication skills Proven excellent problem-solving & story-telling abilities with analytical mindset Proven excellent interpersonal and team skills Preferred Qualifications Understanding & experience of SparkNLP framework Understanding of Responsible Use of AI, model fairness, bias assessment and its risk mitigations Exposure to RAG, LangChain, VectorDBs At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone-of every race, gender, sexuality, age, location and income-deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.

AI / Machine Learning / Data Science part time Work from Home (Any where in world) Warm Greetings from Excel Online Classes, We are a team of industry professionals running an institute that provides comprehensive online IT training, technical support, and development services. We are currently seeking AI / Machine Learning / Data Science Experts who are passionate about technology and can collaborate with us in their free time. If you're enthusiastic, committed, and ready to share your expertise, we would love to work with you! Were hiring for the following services: Online Training Online Development Online Technical Support Conducting Online Interviews Corporate Training Proof of Concept (POC) Projects Research & Development (R&D) We are looking for immediate joiners who can contribute in any of the above areas. If you're interested, please fill out the form using the link below: https://docs.google.com/forms/d/e/1FAIpQLSdvut0tujgMbBIQSc6M7qldtcjv8oL1ob5lBc2AlJNRAgD3Cw/viewform We also welcome referrals! If you know someone—friends, colleagues, or connections—who might be interested in: Teaching, developing, or providing tech support online Sharing domain knowledge (e.g., Banking, Insurance, etc.) Teaching foreign languages (e.g., Spanish, German, etc.) Learning or brushing up on technologies to clear interviews quickly Upskilling in new tools or frameworks for career growth Please feel free to forward this opportunity to them. For any queries, feel free to contact us at: excel.onlineclasses@gmail.com Thank you & Best Regards, Team Excel Online Classes excel.onlineclasses@gmail.com

AI/ML Engineer/Manager: Location – Pune Experience: 6+ years Notice period – Immediate to 30 days. Key Responsibilities: Lead the development of machine learning PoCs and demos using structured/tabular data for use cases such as forecasting, risk scoring, churn prediction, and optimization. Collaborate with sales engineering teams to understand client needs and present ML solutions during pre-sales calls and technical workshops. Build ML workflows using tools such as SageMaker, Azure ML, or Databricks ML and manage training, tuning, evaluation, and model packaging. Apply supervised, unsupervised, and semi-supervised techniques such as XGBoost, CatBoost, k-Means, PCA, time-series models, and more. Work with data engineering teams to define data ingestion, preprocessing, and feature engineering pipelines using Python, Spark, and cloud-native tools. Package and document ML assets so they can be scaled or transitioned into delivery teams post-demo. Stay current with best practices in ML explainability , model performance monitoring , and MLOps practices. Participate in internal knowledge sharing, tooling evaluation, and continuous improvement of lab processes. Qualifications: 8+ years of experience developing and deploying classical machine learning models in production or PoC environments. Strong hands-on experience with Python , pandas , scikit-learn , and ML libraries such as XGBoost, CatBoost , LightGBM, etc. Familiarity with cloud-based ML environments such as AWS SageMaker , Azure ML , or Databricks . Solid understanding of feature engineering, model tuning, cross-validation, and error analysis . Experience with unsupervised learning , clustering, anomaly detection, and dimensionality reduction techniques. Comfortable presenting models and insights to technical and non-technical stakeholders during pre-sales engagements. Working knowledge of MLOps concepts , including model versioning, deployment automation, and drift detection. Interested candidates shall apply or share resumes at kanika.garg@austere.co.in.

Company Overview Docusign brings agreements to life. Over 1.5 million customers and more than a billion people in over 180 countries use Docusign solutions to accelerate the process of doing business and simplify people’s lives. With intelligent agreement management, Docusign unleashes business-critical data that is trapped inside of documents. Until now, these were disconnected from business systems of record, costing businesses time, money, and opportunity. Using Docusign’s Intelligent Agreement Management platform, companies can create, commit, and manage agreements with solutions created by the #1 company in e-signature and contract lifecycle management (CLM). What You'll Do You will play an important role in applying and implementing effective machine learning solutions, with a significant focus on Generative AI. You will work with product and engineering teams to contribute to data-driven product strategies, explore and implement GenAI applications, and deliver impactful insights. This positionis an individual contributor role reporting to the Senior Manager, Data Science. Responsibility Experiment with, apply, and implement DL/ML models, with a strong emphasis on Large Language Models (LLMs), Agentic Frameworks, and other Generative AI techniques to predict user behavior, enhance product features, and improve automation Utilize and adapt various GenAI techniques (e.g., prompt engineering, RAG, fine-tuning existing models) to derive actionable insights, generate content, or create novel user experiences Collaborate with product, engineering, and other teams (e.g., Sales, Marketing, Customer Success) to build Agentic system to run campaigns at-scale Conduct in-depth analysis of customer data, market trends, and user insights to inform the development and improvement of GenAI-powered solutions Partner with product teams to design, administer, and analyze the results of A/B and multivariate tests, particularly for GenAI-driven features Leverage data to develop actionable analytical insights & present findings, including the performance and potential of GenAI models, to stakeholders and team members Communicate models, frameworks (especially those related to GenAI), analysis, and insights effectively with stakeholders and business partners Stay updated on the latest advancements in Generative AI and propose their application to relevant business problems Complete assignments with a sense of urgency and purpose, identify and help resolve roadblocks, and collaborate with cross-functional team members on GenAI initiatives Job Designation Hybrid: Employee divides their time between in-office and remote work. Access to an office location is required. (Frequency: Minimum 2 days per week; may vary by team but will be weekly in-office expectation) Positions at Docusign are assigned a job designation of either In Office, Hybrid or Remote and are specific to the role/job. Preferred job designations are not guaranteed when changing positions within Docusign. Docusign reserves the right to change a position's job designation depending on business needs and as permitted by local law. What You Bring Basic Bachelor's or Master's degree in Computer Science, Physics, Mathematics, Statistics, or a related field 3+ years of hands-on experience in building data science applications and machine learning pipelines, with demonstrable experience in Generative AI projects Experience with Python for research and software development purposes, including common GenAI libraries and frameworks Experience with or exposure to prompt engineering, and utilizing pre-trained LLMs (e.g., via APIs or open-source models) Experience with large datasets, distributed computing, and cloud computing platforms (e.g., AWS, Azure, GCP) Proficiency with relational databases (e.g., SQL) Experience in training, evaluating, and deploying machine learning models in production environments, with an interest in MLOps for GenAI Proven track record in contributing to ML/GenAI projects from ideation through to deployment and iteration Experience using machine learning and deep learning algorithms like CatBoost, XGBoost, LGBM, Feed Forward Networks for classification, regression, and clustering problems, and an understanding of how these can complement GenAI solutions Experience as a Data Scientist, ideally in the SaaS domain with some focus on AI-driven product features Preferred PhD in Statistics, Computer Science, or Engineering with specialization in machine learning, AI, or Statistics, with research or projects in Generative AI 5+ years of prior industry experience, with at least 1-2 years focused on GenAI applications Previous experience applying data science and GenAI techniques to customer success, product development, or user experience optimization Hands-on experience with fine-tuning LLMs or working with RAG methodologies Experience with or knowledge of experimentation platforms (like DataRobot) and other AI related ones (like CrewAI) Experience with or knowledge of the software development lifecycle/agile methodology, particularly in AI product development Experience with or knowledge of Github, JIRA/Confluence Contributions to open-source GenAI projects or a portfolio of GenAI related work Programming Languages like Python, SQL; familiarity with R Strong knowledge of common machine learning, deep learning, and statistics frameworks and concepts, with a specific understanding of Large Language Models (LLMs), transformer architectures, and their applications Ability to break down complex technical concepts (including GenAI) into simple terms to present to diverse, technical, and non-technical audiences Life At Docusign Working here Docusign is committed to building trust and making the world more agreeable for our employees, customers and the communities in which we live and work. You can count on us to listen, be honest, and try our best to do what’s right, every day. At Docusign, everything is equal. We each have a responsibility to ensure every team member has an equal opportunity to succeed, to be heard, to exchange ideas openly, to build lasting relationships, and to do the work of their life. Best of all, you will be able to feel deep pride in the work you do, because your contribution helps us make the world better than we found it. And for that, you’ll be loved by us, our customers, and the world in which we live. Accommodation Docusign is committed to providing reasonable accommodations for qualified individuals with disabilities in our job application procedures. If you need such an accommodation, or a religious accommodation, during the application process, please contact us at accommodations@docusign.com. If you experience any issues, concerns, or technical difficulties during the application process please get in touch with our Talent organization at taops@docusign.com for assistance. Our global benefits Paid time off Take time to unwind with earned days off, plus paid company holidays based on your region. Paid parental leave Take up to six months off with your child after birth, adoption or foster care placement. Full health benefits Options for 100% employer-paid health plans from day one of employment. Retirement plans Select retirement and pension programs with potential for employer contributions. Learning & development Grow your career with coaching, online courses and education reimbursements. Compassionate care leave Paid time off following the loss of a loved one and other life-changing events.

Machine Learning Engineer (L1) Experience Required: 2-4 years As a Machine Learning Engineer at Spring, you’ll help bring data-driven intelligence into our products and operations. You’ll support the development and deployment of models and pipelines that power smarter decisions, more personalized experiences, and scalable automation. This is an opportunity to build hands-on experience in real-world ML and AI systems while collaborating with experienced engineers and data scientists. You’ll work on data processing, model training, and integration tasks — gaining exposure to the entire ML lifecycle, from experimentation to production deployment. You’ll learn how to balance model performance with system requirements, and how to structure your code for reliability, observability, and maintainability. You’ll use modern ML/AI tools such as scikit-learn, HuggingFace, and LLM APIs — and be encouraged to explore AI techniques that improve our workflows or unlock new product value. You’ll also be expected to help build and support automated data pipelines, inference services, and validation tools as part of your contributions. You’ll work closely with engineering, product, and business stakeholders to understand how models drive value. Over time, you’ll build the skills and judgment needed to identify impactful use cases, communicate technical trade-offs, and contribute to the broader evolution of ML at Spring. What You’ll Do Support model development and deployment across structured and unstructured data and AI use cases. Build and maintain automated pipelines for data processing, training, and inference. Use ML and AI tools (e.g., scikit-learn, LLM APIs) in day-to-day development. Collaborate with engineers, data scientists, and product teams to scope and deliver features. Participate in code reviews, testing, and monitoring practices. Integrate ML systems into customer-facing applications and internal tools. Identify differences in data distribution that could affect model performance in real-world applications. Stay up to date with developments in the machine learning industry. Tech Expectations Core Skills Curiosity, attention to detail, strong debugging skills, and eagerness to learn through feedback Solid foundation in statistics and data interpretation Strong understanding of data structures, algorithms, and software development best practices Exposure to data pipelines, model training and evaluation, or training workflows Languages Must Have: Python, SQL ML Algorithms Must Have: Traditional modeling techniques (e.g., tree models, Naive Bayes, logistic regression) Ensemble methods (e.g., XGBoost, Random Forest, CatBoost, LightGBM) ML Libraries / Frameworks Must Have: scikit-learn, Hugging Face, Statsmodels, Optuna Good to Have: SHAP, Pytest Data Processing / Manipulation Must Have: pandas, NumPy Data Visualization Must Have: Plotly, Matplotlib Version Control Must Have: Git Others – Good to Have AWS (e.g., EC2, SageMaker, Lambda) Docker Airflow MLflow Github Actions

Title: Senior Machine Learning Engineer Location: Gurgaon, IN Type: (Hybrid, In-Office) Job Description Who We Are: Fareportal is a travel technology company powering a next-generation travel concierge service. Utilizing its innovative technology and company owned and operated global contact centres, Fareportal has built strong industry partnerships providing customers access to over 500 airlines, a million lodgings, and hundreds of car rental companies around the globe. With a portfolio of consumer travel brands including CheapOair and OneTravel, Fareportal enables consumers to book-online, on mobile apps for iOS and Android, by phone, or live chat. Fareportal provides its airline partners with access to a broad customer base that books high-yielding international travel and add-on ancillaries. HIGHLIGHTS : Fareportal is the number 1 privately held online travel company in flight volume. Fareportal partners with over 500 airlines, 1 million lodgings, and hundreds of car rental companies worldwide. 2019 annual sales exceeded $5 billion. Fareportal sees over 150 million unique visitors annually to our desktop and mobile sites. Fareportal, with its global workforce of over 2,600 employees, is strategically positioned with 9 offices in 6 countries and headquartered in New York City. What We Do Our Machine Learning team is at the forefront of developing state-of-the-art models and solutions that drive key business decisions, improve customer experiences, and streamline operations. We work on diverse projects, from personalized recommendations and predictive modelling to call analytics and business forecasting, using cutting-edge technology and data-driven insights. Our department is unique in its end-to-end approach to problem-solving, focusing on innovation, collaboration, and impactful results. As a Senior Machine Learning Engineer, you will play a crucial role in the complete lifecycle of our Machine Learning projects. You will: - Engage with stakeholders to gather and refine requirements. - Perform exploratory data analysis (EDA) and manipulate data using SQL and NoSQL databases. - Engineer and select features, ensuring optimal model performance. - Train, test, and fine-tune models using advanced algorithms and methodologies. - Deploy models into production environments using frameworks like Docker, Kubernetes, and Rest APIs. - Monitor and evaluate model performance, including A/B testing and continuous improvement. - Collaborate with cross-functional teams, ensuring seamless integration and communication throughout the project lifecycle. In this Role You Will Get To - Work within a dynamic and talented team of engineers and data scientists to build scalable Machine Learning solutions that impact thousands of users daily. - Develop, deploy, and maintain Machine Learning models that address complex business challenges and enhance customer engagement. - Participate in brainstorming sessions, technical design discussions, and collaborate with product managers, data engineers, and other stakeholders to ensure alignment and success. - Explore and implement new technologies, including cloud-based (AWS, Azure) and on-prem systems, to optimize and enhance ML model performance. - Conduct rigorous testing and monitoring to ensure the reliability and accuracy of models. Who You Are Must-Haves: - Strong communication skills, capable of translating technical concepts into actionable business insights. - Proven experience (2-4 years) as a Data Scientist, Machine Learning Engineer, or in a similar role. - Proficient in SQL querying for data extraction and manipulation. - Experience with high volume structured data, including data manipulation, exploratory data analysis (EDA), and data modelling. - Strong proficiency in Python programming and familiarity with Rest API frameworks for seamless model integration. - Demonstrated ability to build and deploy at least 3-5 ML models in production environments. - Hands-on experience with Regression, Classification, Recommendation algorithms, and Neural Networks. - Expertise in relevant ML libraries such as CatBoost, XGBoost, LightGBM, Scikit-learn, TensorFlow, and PyTorch. - Knowledge of containerization technologies (e.g., Docker, Kubernetes) and virtual machines (VMs). - Familiarity with cloud (AWS/Azure) and on-prem data systems. - Bachelors in computer science, or a related field; a Master's degree is a plus. Good-to-Haves: - Experience working with distributed data processing frameworks like PySpark. - Experience working with Natural Language Processing (NLP) and Large Language Models (LLMs). - Familiarity with MLOps platforms such as MLFlow and Kubeflow. - Prior experience with A/B testing methodologies. - Advanced skills in data visualization tools like Tableau or Power BI. Disclaimer This job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee. Fareportal reserves the right to change the job duties, responsibilities, expectations, or requirements posted here at any time at the Company's sole discretion, with or without notice.