37 Lightgbm Jobs

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.,

Role Overview: You will be a data scientist responsible for developing and deploying impactful data solutions using your expertise in machine learning and statistical analysis. Your role will involve designing and implementing predictive models, collaborating with stakeholders, and driving strategic decisions through experimentation and data-driven insights. Key Responsibilities: - Design and implement robust machine learning solutions including regression, classification, NLP, time series, and recommendation systems. - Evaluate and tune models using appropriate metrics like AUC, RMSE, precision/recall, etc. - Work on feature engineering, model interpretability, and performance optimization. - Partner with stakeholders to identify key opportunities where data science can drive business value. - Translate business problems into data science projects with clearly defined deliverables and success metrics. - Provide actionable recommendations based on data analysis and model outputs. - Conduct deep-dive exploratory analysis to uncover trends and insights. - Apply statistical methods to test hypotheses, forecast trends, and measure campaign effectiveness. - Design and analyze A/B tests and other experiments to support product and marketing decisions. - Automate data pipelines and dashboards for ongoing monitoring of model and business performance. Qualification Required: - Proficiency in Python and MySQL. - Experience with libraries/frameworks such as scikit-learn, Pandas, NumPy, Time series, ARIMA, Bayesian model, Market Mix model, Regression, XGBoost, LightGBM, TensorFlow, PyTorch. - Proficient in statistical techniques like hypothesis testing, regression analysis, and time-series analysis. - Familiarity with databases like PostgreSQL, BigQuery, and MySQL. - Knowledge of visualization tools like Plotly, Seaborn, and Matplotlib. - Experience in Gitlab, CI/CD pipelines is a plus. - Familiarity with cloud platforms like AWS, GCP, or Azure services is also advantageous. In case of any additional details of the company, they are not provided in the job description.,

About Chargebee: Chargebee is a subscription billing and revenue management platform powering some of the fastest-growing brands around the world today, including Calendly, Hopin, Pret-a-Manger, Freshworks, Okta, Study.com and others. Thousands of SaaS and subscription-first businesses process over billions of dollars in revenue every year through the Chargebee platform. Headquartered in San Francisco, USA, our 500+ team members work remotely throughout the world, including India, the Netherlands, Paris, Spain, Australia, and the USA. Chargebee has raised over $480 million in capital and is funded by Accel, Tiger Global, Insight Partners, Steadview Capital, and Sapphire Ventures. And were on a mission to push the boundaries of subscription revenue operations. Not just ours, but every customer and prospective business on a recurring revenue model. Our team builds high-quality and innovative software to enable our customers to grow their revenues powered by the state-of-the-art subscription management platform. Key Roles & Responsibilities Productionise ML workflows : build and maintain data pipelines for feature generation, ML model training, batch scoring, and real?time inference using modern orchestration and container frameworks. Own model serving infrastructure : implement fast, reliable APIs / batch jobs; manage autoscaling, versioning, and rollback strategies Feature?store development : design and operate feature stores and corresponding data pipelines to guarantee trainingserving consistency. CI/CD & DevEx : automate testing, deployment, and monitoring of data and model artefacts; provide templated repos and documentation that let data scientists move from notebook to prod quickly. Observability & quality : instrument data?drift, concept?drift, and performance metrics; set up alerting dashboards to ensure model health. Collaboration & review : work closely with data scientists on model experimentation, production?harden their code, review PRs, and evangelise MLOps best practices across the organisation. Required Skills & Experience 3+ years as a ML / Data Engineer working on large-scale, data-intensive systems in cloud environments (AWS, GCP, or Azure), with proven experience partnering closely with ML teams to deploy models at scale. Proficient in Python plus one of Go / Java / Scala; strong software?engineering fundamentals (testing, design patterns, code review). Hands on experience in Spark and familiarity with streaming frameworks (Kafka, Flink, Spark Structured Streaming) Hands-on experience with workflow orchestrators (Airflow, Dagster, Kubeflow Pipelines, etc.) and container platforms (Docker + Kubernetes/EKS/ECS). Practical knowledge of ML algorithms like XGBoost, LightGBM, transformers and deep learning frameworks like pytorch is preferred Experience with experiment?tracking / ML model?management tools (MLflow, SageMaker, Vertex AI, Weights & Biases) is a plus Benefits: Want to know what it means to work for a company that genuinely cares about you Check out just a few of the benefits we give our employees: We are Globally Local With a diverse team across four continents, and customers in over 60 countries, you get to work closely with a global perspective right from your own neighborhood. We value Curiosity We believe the next great idea might just be around the corner. Perhaps its that random thought you had ten minutes ago. We believe in creating an ecosystem that fosters a desire to seek out hard questions, and then figure out answers to them. Customer! Customer! Customer! Everything we do is driven towards enabling our customers growth. This means no matter what you do, you will always be adding real value to a real business problem. Its a lot of responsibility, but also a lot of fun. If you resonate with Chargebee, have a monstrous appetite for curiosity, and an insatiable urge to learn and build new things, were waiting for you! We value people from all backgrounds and are dedicated to hiring and employing a diverse and inclusive workplace. Come be a part of the Chargebee tribe! Show more Show less

JOB DESCRIPTION: AI Associate-Machine Learning Location: Bengaluru | Experience: 1-3 Years ABOUT NEWCOLD NewCold is a service provider in cold chain logistics with a focus on development and operation of large, highly automated cold stores. NewCold strives to be crucial in the cold chain of leading food companies, by offering advanced logistic services worldwide. NewCold is one of the fastest growing companies (over 2,000 employees) in the cold chain logistics and they are expanding teams to support this growth. They use the latest technology that empowers people, to handle food responsibly and guarantee food safety in a sustainable way. They challenge the industry, believe in long-term partnerships, and deliver solid investment opportunities that enable next generation logistic solutions. NewCold has leading market in-house expertise in designing, engineering, developing and operating state-of-the-art automated cold stores: a result of successful development and operation of over 15 automated warehouses across three continents. With the prospect of many new construction projects around the world in the very near future, this vacancy offers an interesting opportunity to join an internationally growing and ambitious organization. Position: AI Associate Machine Learning Join our growing AI team as an AI Associate (ML) and build real-world machine learning solutions that directly impact business performance. This is an exciting opportunity to work with experienced professionals, apply your skills to live data problems, and grow your career in a fast-paced, collaborative environment. Your Role: Youll help design, train, and deploy ML models that power applications in supply chain, logistics, finance, and operations. From predicting delivery times to detecting anomalies in large datasets, your work will drive smarter decision-making. What Youll Do: Build ML models for forecasting, classification, clustering, and anomaly detection using real business data. Work on the full ML lifecycle: data prep, feature engineering, model selection, evaluation, and deployment. Collaborate with cross-functional teams (engineering, data, operations) to understand business needs and build ML-driven solutions. Deploy and monitor models in production environments using APIs and MLOps tools. Document experiments and contribute to reusable ML components and workflows. What Were Looking For: B.Tech / M.Tech in Computer Science, Engineering, or related fields. 1 -2 years of experience applying machine learning in real-world scenarios. Strong programming skills in Python and familiarity with ML libraries (scikit-learn, XGBoost, LightGBM, PyTorch, etc.). Hands-on experience working with structured and semi-structured data. Familiarity with cloud platforms (Azure preferred) and tools for ML deployment. Bonus: Experience with time series, anomaly detection, or working in supply chain/logistics. Why Join Us Be part of a growing AI team solving real industry challenges. Work on high-impact projects with supportive mentors and strong learning culture. Gain experience in production ML pipelines and cloud deployment at scale. Access opportunities to grow in computer vision, LLMs, or advanced MLOps. Show more Show less

The ML Solutions team within Markets OPS Technology is dedicated to developing solutions using Artificial Intelligence, Machine Learning, and Generative AI. This team is a leader in creating new ideas, innovative technology solutions, and ground-breaking solutions for Markets Operations and Other Line of Businesses. We work closely with our clients and business partners to progress solutions from ideation to production by leveraging the entrepreneurial spirit and technological excellence. The ML Solutions team is seeking a Data Scientist/Machine Learning Engineer to drive the design, development, and deployment of innovative AI/ML and GenAI-based solutions. In this hands-on role, you will leverage your expertise to create a variety of AI models, guiding a team from initial concept to successful production. A key aspect involves mentoring team members and fostering their growth. You will collaborate closely with business partners and stakeholders, championing the adoption of these advanced technologies to enhance client experiences, deliver tangible value to our customers, and ensure adherence to regulatory requirements through cutting-edge technical solutions. This position offers a unique opportunity to shape the future of our AI initiatives and make a significant impact on the organization. Key Responsibilities: - **Hands-On Execution and Delivery:** Actively contribute to the development and delivery of AI solutions, driving innovation and excellence within the team. Take a hands-on approach to ensure AI models are successfully deployed into production environments, meeting high-quality standards and performance benchmarks. - **Mentoring Young Talents:** Mentoring the team, guiding data analysts/ML engineers from concept to production. This involves fostering technical growth, providing project oversight, and ensuring adherence to best practices, ultimately building a high-performing and innovative team. - **Quality Control:** Ensure the quality and performance of generative AI models, conducting rigorous testing and evaluation. - **Research and Development:** Participate in research activities to explore and advance state-of-the-art generative AI techniques. Stay actively engaged in monitoring ongoing research efforts, keeping abreast of emerging trends, and ensuring that the Generative AI team remains at the forefront of the field. - **Cross-Functional Collaboration:** Collaborate effectively with various teams, including product managers, engineers, and data scientists, to integrate AI technologies into products and services. Skills & Qualifications: - 8 to 13 years of Strong hands-on experience in Machine Learning, delivering complex solutions to production. - Experience with Generative AI technologies essential. - Understanding of concepts like supervised, unsupervised, clustering, embedding. - Knowledge of NLP, Name Entity Recognition, Computer Vision, Transformers, Large Language Models. - In-depth knowledge of deep learning and Generative AI frameworks such as Langchain, Lang Graph, Crew AI, or similar. - Experience with other open-source frameworks/libraries/APIs like Hugging Face Transformers, Spacy, Pandas, scikit-learn, NumPy, OpenCV. - Experience in using Machine Learning/Deep Learning: XGBoost, LightGBM, TensorFlow, PyTorch, Keras. - Proficiency in Python Software Development, following Object-Oriented design patterns and best practices. - Strong background in mathematics: linear algebra, probability, statistics, and optimization. - Experience with evaluation, scoring with frameworks like ML Flow. - Experience with Docker containers and editing a Docker file, experience with K8s is a plus. - Experience with Postgres and Vector DBs a plus. - Excellent problem-solving skills and the ability to think creatively. - Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams. - Publications and contributions to the AI research community are a plus. - Masters degree/Ph.D. or equivalent experience in Computer Science, Data Science, Statistics, or a related field. - 8-12 years of experience This job description provides a high-level review of the types of work performed. Other job-related duties may be assigned as required.,

Job Summary: We are looking for a highly skilled and experienced AI/ML Developer-Lead with 4-5 years of hands-on relevant experience to join our technology team. You will be responsible for designing, developing, and optimizing machine learning models that drive intelligent business solutions. The role involves close collaboration with cross-functional teams to deploy scalable AI systems and stay abreast of evolving trends in artificial intelligence and machine learning. Key Responsibilities: 1. Develop and Implement AI/ML Models Design, build, and implement AI/ML models tailored to solve specific business challenges, including but not limited to natural language processing (NLP), image recognition, recommendation systems, and predictive analytics. 2. Model Optimisation and Evaluation Continuously improve existing models for performance, accuracy, and scalability. 3. Data Preprocessing and Feature Engineering Collect, clean, and preprocess structured and unstructured data from various sources. Engineer relevant features to improve model performance and interpret ability. 4. Collaboration and Communication Collaborate closely with data scientists, back end engineers, product managers, and stakeholders to align model development with business goals. Communicate technical insights clearly to both technical and non-technical stakeholders. 5. Model Deployment and Monitoring Deploy models to production using MLOps practices and tools (e.g., MLflow, Docker, Kubernetes). Monitor live model performance, diagnose issues, and implement improvements as needed. 6. Staying Current with AI/ML Advancements Stay informed of current research, tools, and trends in AI and machine learning. Evaluate and recommend emerging technologies to maintain innovation within the team. 7. Code Reviews and Best Practices Participate in code reviews to ensure code quality, scalability, and adherence to best practices. Promote knowledge sharing and mentoring within the development team. Required Skills and Qualifications: Bachelors or Masters degree in computer science, Data Science, Engineering, or a related field. 4-5 years' of experience in machine learning, artificial intelligence, or applied data science roles. Strong programming skills in Python (preferred) and/or R. Proficiency in ML libraries and frameworks , including: scikit-learn, XGBoost, LightGBM, TensorFlow or Keras, PyTorch Skilled in data preprocessing and feature engineering , using; pandas, numpy, sklearn.preprocessing Practical experience in deploying ML models into production environments using REST APIs and containers. Familiarity with version control systems (e.g., Git) and containerization tools (e.g., Docker). Experience working with cloud platforms such as AWS, Google Cloud Platform (GCP), or Azure . Understanding software development methodologies , especially Agile/Scrum. Strong analytical thinking, debugging, and problem-solving skills in real-world AI/ML applications.

Job Title: Data Science/MLOps Engineer- Retail/CPG Domain Location: India (Remote/Hybrid - [Insert City]) Experience Level: 3-5 Years Employment Type: Full-Time Job Summary We are seeking a highly skilled and motivated Data Science & MLOps Engineer to join our growing team. This role combines expertise in traditional machine learning, model deployment, and scalable MLOps infrastructure to drive business impact in the Retail and Consumer Packaged Goods (CPG) domain. The ideal candidate will have a strong background in building and operationalizing end-to-end ML pipelines and delivering production-grade ML solutions aligned with real-world use cases such as demand forecasting, pricing optimization, churn prediction, and personalization. Key Responsibilities Design and develop robust machine learning models for a variety of use cases including prediction, classification, segmentation, and time-series forecasting. Build, automate, and monitor ML pipelines using tools such as MLflow, Kubeflow, Airflow, Metaflow , or equivalent. Collaborate with data engineers and domain experts to transform business problems into scalable ML solutions. Deploy models into production environments using containerization (Docker) and orchestration frameworks (Kubernetes, AWS SageMaker, Azure ML, GCP Vertex AI). Set up and maintain model versioning, tracking, retraining workflows, and CI/CD pipelines for ML systems. Implement testing, monitoring, and performance tracking for deployed models to ensure reliability, fairness, and accuracy. Collaborate with DevOps and platform teams to improve infrastructure, scalability, and cost-efficiency of ML workloads. Stay updated on developments in MLOps, traditional ML algorithms, and best practices for production machine learning. Required Qualifications Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field. 3+ years of experience in developing, deploying, and maintaining ML models in production environments. Strong programming skills in Python,SQL , with experience using libraries such as scikit-learn, pandas, NumPy, XGBoost, LightGBM, Prophet, or TensorFlow/PyTorch for traditional ML. Hands-on experience with ML workflow tools like MLflow, Kubeflow Pipelines, Airflow, or SageMaker Pipelines. Experience deploying ML models using Docker, Kubernetes , or cloud-native ML platforms (AWS, Azure, GCP). Good understanding of EDA , model evaluation techniques, feature engineering, and data preprocessing for structured and semi-structured data. Proven ability to align ML solutions with retail/CPG business requirements and deliver measurable outcomes. Preferred Qualifications Experience with time-series modeling , prophet , recommendation systems, or demand forecasting in retail or CPG. Familiarity with model drift detection, automated retraining , and data versioning using tools like DVC or LakeFS. Exposure to CI/CD practices for ML , including testing, rollback strategies, and reproducibility. Why Join Us Be at the forefront of innovation by applying GenAI to real-world retail/CPG challenges. Work in a dynamic, fast-paced, and intellectually stimulating environment. Collaborate with cross-functional teams across data science, engineering, and domain consulting. Enjoy flexibility through a hybrid/remote work model and opportunities for continuous learning.

As the Principal Machine Learning Scientist at Nykaa's AdTech Data Science team, you will play a crucial role in developing intelligent, ML-powered advertising products such as Sponsored Products, Display Ads, and Creative Personalization. Your primary responsibility will be to lead the development of foundational ML models and systems that drive the ad platform, collaborating closely with product, engineering, and business teams to address high-impact problems in advertising science. You should have a solid educational background with a Bachelors, Masters, or Ph.D. in Computer Science, Machine Learning, Statistics, or a related technical field from a reputable institution. With over 10 years of experience in building production-grade ML systems, including at least 5 years in AdTech, performance marketing, or large-scale recommender systems, you should have a proven track record of delivering impactful ML innovations as a senior individual contributor. Your technical expertise should encompass domains such as CTR/CVR prediction, bidding dynamics, bid/budget optimization, multi-objective learning, campaign automation, target ROAS modeling, and generative AI for creative optimization. Proficiency in ML algorithms, reinforcement learning, Bayesian optimization, and game theory as applied to digital advertising is essential, along with fluency in Python and ML frameworks like PyTorch, TensorFlow, XGBoost, and libraries like LightGBM and Scikit-learn. In addition to technical skills, you should possess a passion for solving complex problems with scientific rigor and business impact, along with a deep ownership mentality and the ability to drive projects independently. Effective communication of technical concepts to cross-functional stakeholders, collaborative mindset, and a track record of influencing product direction through technical thought leadership are also crucial soft skills for this role. Your responsibilities will include designing and developing cutting-edge models for predicting ad outcomes, building intelligent automation systems for campaign management, developing generative models for optimizing ad creatives, owning the architecture of core components within the ad platform, and collaborating with Product and Engineering teams to design ML-first features that are measurable. Additionally, you will explore and prototype novel approaches using Reinforcement Learning, RLHF, contrastive learning, or LLMs for advertiser assistance or campaign agents, and contribute to thought leadership and innovation through internal whitepapers and external publications. Joining Nykaa's AdTech team will offer you the opportunity to be a key technical architect behind the company's growing AdTech ecosystem, work on cutting-edge problems at the intersection of ML, economics, personalization, and creative optimization, and shape the future of advertising on India's leading beauty and lifestyle platform with a focus on user delight and advertiser success.,

The position is based at Altimetrik with base locations in Bangalore, Chennai, Pune, Jaipur, Hyderabad, Gurugram. The ideal candidate should be able to join immediately or within 10 days. As a Machine Learning Engineer, your primary responsibility will be to design and implement ML solutions while architecting scalable and efficient systems. You should be proficient in Machine Learning Algorithms, Data Engineering, ETL/ELT processes, data cleaning, preprocessing, EDA, feature engineering, data splitting, and encoding. Additionally, you should have experience in MLOps, including model versioning, training, experimenting, deployment, and monitoring using tools such as Python, Pandas, TensorFlow, PyTorch, Scikit-learn, Keras, XGBoost, LightGBM, Matplotlib, R, Scala, Java, Git, DVC, MLFlow, Kubernetes, Kubeflow, Docker, Containers, CI/CD deployments, Apache Airflow, Databricks, Snowflake, Salesforce, SAP, AWS/Azure/GCP Data Cloud Platforms, AWS SageMaker, Google AI Platform, Azure Machine Learning, model design and optimization, LLMs models (OpenAI, BERT, LLaMA, Gemini, etc.), RDBMS, NoSQL databases, Vector DB, RAG Pipelines, AI Agent Frameworks, AI agent authentication, deployment, AI security and compliance, and Prompt Engineering. Your secondary skills should include cloud computing, data engineering, and DevOps. You will be responsible for designing and developing AI/ML models and algorithms. Collaboration with data scientists and engineers to ensure the scalability and performance of AI/ML systems will be a key part of your role. To be considered for this position, you should have 12-15 years of experience in AI/ML development, strong expertise in AI/ML frameworks and tools, and excellent problem-solving and technical skills.,

We are building Saulikh, a platform transforming agri-trade with data science and AI. Our goal is to bring trust, transparency, and intelligence to commodity trading at scale. Role Overview We are seeking a Data Scientist to lead data-driven initiatives across trade, pricing, risk, and logistics. The role involves working with real-world datasets, building dashboards, applying ML models, and assisting in the development of prototypes for trust scoring, fraud detection, and forecasting. Key Responsibilities Collect, clean, and preprocess financial, transactional, and behavioral datasets from multiple sources. Design and implement trust/credit scoring algorithms that assess buyer/seller reliability and default risk. Engineer features from trade history, payment timeliness, disputes, cancellations, and external market data. Apply statistical models and machine learning techniques (logistic regression, random forests, gradient boosting, neural networks) to predict trustworthiness. Develop scoring frameworks normalized to a range (e.g., 01000), similar to CIBIL standards. Validate and back-test models against historical data to ensure accuracy, fairness, and stability. Build monitoring pipelines to track score performance, drift, and anomalies in real time. Work with business and product teams to ensure scores align with practical trade and risk management requirements. Document methodologies and communicate insights to technical and non-technical stakeholders. Requirements Bachelors or Masters degree in Computer Science, Statistics, Mathematics, Economics, or a related field. 25 years of experience as a Data Scientist, preferably in credit scoring, risk modeling, or fraud detection. Strong proficiency in Python (pandas, numpy, scikit-learn, statsmodels) or R. Experience with machine learning algorithms such as logistic regression, decision trees, random forests, gradient boosting (XGBoost/LightGBM), and neural networks. Strong SQL skills for handling large datasets; experience with data pipelines and API integrations. Domain knowledge of credit scoring principles, risk assessment, and regulatory considerations . Experience in model validation, back-testing, and ensuring fairness and interpretability. Excellent analytical and problem-solving skills with attention to detail. Strong communication skills for presenting findings to technical and business stakeholders. The interested candidates may apply here. Show more Show less

**We are currently hiring for a senior-level position and are looking for immediate joiners only. If you are interested, please send your updated resume to [HIDDEN TEXT] along with details of your CTC, ECTC and notice period. Also please provide a brief of your experience on Predictive Analytics and Machine learning, including the total number of years of hands-on experience in these areas. ** About the Role We are looking for a visionary Senior Data Scientist who excels in predictive analytics and machine learning. You will lead the design, development, deployment, and optimization of data-driven products that drive high business impactsuch as forecasting the price/value of stocks, commodities, or consumer goods (e.g., apparel, retail). This role is for someone who has successfully taken machine learning models from concept to production, iteratively improved them based on user feedback and real-world performance, and thrives on delivering measurable results. Key Responsibilities End-to-End Product Ownership: Lead the development of predictive models from exploration and prototype to full-scale production deployment. Forecasting & Prediction: Build robust time-series and regression models to predict prices/values of financial assets, oil, apparel, and other commodities. Model Optimization: Continuously monitor and fine-tune models for accuracy, performance, and scalability using real-time data feedback. ML Ops & Deployment: Collaborate with engineering to ensure successful deployment and monitoring of models in production environments. Stakeholder Collaboration: Translate business problems into analytical frameworks, working closely with product, strategy, and business teams. Data Strategy: Define and manage pipelines and feature stores using structured and unstructured data sources. Mentorship: Guide and mentor junior data scientists and analysts in best practices and advanced modeling techniques. Required Qualifications 8+ years of experience in data science, with a strong background in predictive analytics and machine learning. Proven experience building and scaling ML models in production environments (not just notebooks or PoCs). Deep expertise in Python, SQL, and ML libraries (e.g., scikit-learn, XGBoost, LightGBM, TensorFlow/PyTorch). Strong knowledge of time-series forecasting, regression techniques, and feature engineering. Experience in domains such as finance, commodities, retail pricing, or demand prediction is highly preferred. Experience working with cloud platforms (AWS, GCP, or Azure) and tools like Airflow, Docker, and MLflow. Ability to define success metrics, conduct A/B tests, and iterate based on measurable KPIs. Excellent communication and storytelling skills with both technical and non-technical stakeholders. Preferred Experience with LLMs, deep learning, or hybrid modeling approaches. Familiarity with data privacy, compliance, and governance in production systems. Publications or thought leadership in applied machine learning or forecasting. Why Join Us Work on high-impact projects with massive data volumes. Shape predictive products that directly influence strategic business outcomes. Join a collaborative, data-first culture with real ownership and innovation. Show more Show less

You are a skilled and innovative AI Developer with hands-on experience in OpenAI APIs, HuggingFace Transformers, and prompt engineering. Your main responsibility will be to design, develop, and deploy intelligent systems for text, image, and code generation by utilizing modern AI technologies and large language models (LLMs). In this role, you will play a crucial part in constructing conversational agents, generative pipelines, and integrating AI into real-world web applications. You will be tasked with utilizing OpenAI APIs such as ChatGPT and DALLE for advanced natural language and image generation tasks. Additionally, you will implement and fine-tune pre-trained models using HuggingFace Transformers. Your role will involve designing optimal prompts for LLMs to enhance model output accuracy and relevance. You are expected to develop and integrate embedding-based search systems employing OpenAI Embeddings, FAISS, or Pinecone. Moreover, you will be responsible for building scalable content generation pipelines for text, image, and code automation. Your tasks will also include fine-tuning of LLMs using LoRA, PEFT, and HuggingFace Trainer APIs, applying NLP techniques with tools like spaCy, NLTK, and Transformers, and developing intelligent chatbots using platforms like Rasa, Dialogflow, or Microsoft Bot Framework. You will integrate AI solutions into web applications using React.js and manage user session and context flow to provide personalized and coherent conversational experiences. Additionally, you will handle data preprocessing using Pandas, regex, and custom scripts, and utilize scikit-learn, XGBoost, and LightGBM for classical ML tasks when necessary. For this role, you are required to hold a Bachelors or Masters degree in Computer Science, AI, Data Science, or a related field. You should have proven experience with OpenAI GPT-based models and image generation APIs, expertise in Prompt Engineering and model behavior optimization, as well as solid experience with embedding-based search and vector databases. Strong command over React.js and JavaScript for frontend-AI integration, a good understanding of ML frameworks and model deployment workflows, and excellent communication and problem-solving skills are essential. Preferred qualifications include experience deploying AI models in production environments, familiarity with LangChain or similar LLM orchestration tools, and previous work on projects involving Generative AI, conversational AI, or custom LLM training.,

Proficiency in machine learning, AI, and statistical modeling techniques, with a strong track record of developing and deploying successful predictive models in a business setting. Strong proficiency in Python and SQL and experience with data science libraries and tools such as: Spark, scikit-learn, XGBoost, LightGBM etc. Proficiency in Neural Networks is an added advantage. Familiarity with standard software engineering practices and tools including object-oriented programming, test-driven development, CI/CD, git, task orchestration (Airflow) and AWS tooling. Strong understanding of credit risk management, lending practices, and regulatory requirements within the financial industry. Good communication and presentation skills.

The ideal candidate for the position should have a Master's degree in Computer Science, Data Science, or a related field, with a preference for a Ph.D. They should have 8-12 years of experience in a Data Scientist or equivalent role, including at least 4 years of specialized experience in Generative AI. This experience should involve leading technical development, mentoring teams, and troubleshooting and debugging GenAI models in production. Additionally, candidates should have experience working with financial data, applying NLP techniques, developing and testing Python code for GenAI solutions, integrating with vector databases, and monitoring MLOps pipelines. It is essential for the candidate to stay up-to-date with the rapidly evolving GenAI landscape, continuously learn new tools and techniques, and communicate technical concepts clearly to non-technical stakeholders. Candidates must possess demonstrable experience in the full lifecycle of real-world, production-level GenAI project implementation, including deploying, monitoring, and maintaining models in a live environment. Proof-of-concept work alone is not sufficient. Candidates should have expertise in various technical areas such as data structures, algorithms, object-oriented programming, scientific computing (NumPy, Pandas, SciPy), machine learning (Scikit-learn, XGBoost, LightGBM), deep learning (TensorFlow, PyTorch), and GenAI (Transformers, LangChain). Specific skills in LLM architectures, RAG, Advanced Chatbot Architectures, Prompt Engineering, Fine-tuning LLMs, and API Development are required. Expert-level Python skills are mandatory, including proficiency in core Python and key libraries for AI/ML and GenAI applications. The candidate should have proven abilities in designing, developing, and deploying complex GenAI projects, a deep understanding of LLMs, RAG, and advanced chatbot architectures, and experience in designing scalable API architectures for GenAI applications. Preferred qualifications include experience with MLOps, cloud computing platforms, conversational AI solutions, research contributions in Generative AI, and building and managing large datasets for training GenAI models. Strong technical leadership, mentorship skills, and excellent communication and collaboration skills are also essential for this role. Overall, the candidate should be able to articulate the challenges and successes of deploying GenAI solutions at scale, effectively convey technical concepts to diverse audiences, and work collaboratively with stakeholders to meet their needs.,

Our Company: Unify Technologies Our Website: http://unifytech.com/ Linked In: https://www.linkedin.com/company/9206998 Offices in: Hyderabad, Bangalore, Pune, Gurgaon India, and Seattle-USA Industry/Domain: Product Engineering Big Data, Healthcare, Cyber Security, Mobile. Few words about Unify Technologies: Unify a Digital Engineering company. We help our clients by providing Digital Engineering Services to develop high-quality products. We have extensive experience in software product engineering and a successful track record of delivering on aggressive delivery plans without compromising on the quality in Cloud, Mobile, and Data practices. Job Title: Experience : (8-10Yrs) - Remote Work - Full Time - Permanent Role Tech Lead AI/ML for Operational Performance & Risk Prediction Location: Work From Home (India) Duration: Permanent Role Engagement Type: Full-time Immediate Joiners Only Role Overview : We are seeking a hands-on Tech Lead to own the architecture, development, and delivery of AI-driven solutions that analyze operational performance, predict risks, and optimize workforce efficiency. This is a permanent position for someone who thrives on solving complex business problems with AI/ML, leading small agile teams, and delivering measurable results in a client-facing environment. Must-Have Skills & Experience Technical Expertise AI/ML Modeling: Strong experience with XGBoost, LightGBM, and Facebook Prophet (time series forecasting). Proven track record in predictive analytics for operational performance and risk modeling. Hands-on experience in model explainability (e.g., SHAP, LIME). Data Engineering & Integration: Proficiency in Python (Pandas, NumPy, scikit-learn, PyTorch/TensorFlow basics). ETL pipeline development using Azure Data Factory or Apache Airflow. Experience with SQL (PostgreSQL / MySQL / MS SQL Server). Cloud Stack: Azure (Data Lake, Synapse, Machine Learning Studio) must be proficient. Deployment of ML models via Azure ML endpoints or containerized solutions. MLOps & Versioning: GitHub/GitLab CI/CD integration. Model lifecycle management & experiment tracking (MLflow, DVC). Domain Knowledge Understanding of field workforce performance metrics, compliance, and KPI-based vendor evaluation. Ability to design a balanced scorecard mapping operational KPIs to financial outcomes. Leadership & Delivery Minimum 810 years of total experience, with 3+ years in a Tech Lead or Solution Architect role. Experience leading small, agile AI/ML delivery teams (35 engineers). Ability to engage with SMEs and translate business goals into technical deliverables. Strong communication skills for working with both technical and operations stakeholders. Nice-to-Have Skills Experience with synthetic data generation for AI training. Knowledge of audio analytics for operational or maintenance insights. Exposure to open-source RAG frameworks (LangChain, Haystack) for operational intelligence. Familiarity with rules-engine integration (e.g., Drools) for hybrid AI + deterministic logic solutions. Key Responsibilities Lead solution design, architecture, and technical delivery of AI-driven operational insights. Ingest and preprocess historical operational, safety, and financial datasets. Build predictive models for workforce performance, delays, and compliance risks. Implement model explainability for stakeholder trust and adoption. Integrate outputs into an Azure-based environment. Ensure data security and compliance with NDA and IT policies. Conduct knowledge transfer to internal and client teams. Engagement Expectations Immediate joiners only must be ready to start within 5 business days. Availability for daily standups and weekly stakeholder reviews. Flexible to work in overlapping hours with US Pacific Time for stakeholder sync. Good to have: Tier 1 and company candidate

As an AI Lead, you will be responsible for driving the development and deployment of cutting-edge AI/ML solutions across the organization. You will collaborate with business stakeholders, data scientists, engineers, and product teams to design scalable, ethical, and impactful AI models. Additionally, you will mentor junior members, guide architectural decisions, and lead AI initiatives from concept to production. Experience in the BFSI sector is mandatory for this role. Key Responsibilities: AI Strategy & Leadership: - Define and execute the AI roadmap aligned with business objectives. - Identify and evaluate AI use cases across business units. - Present solution architectures, POCs, and business impact assessments to leadership. Model Development & Deployment: - Lead the design, development, and deployment of ML/AI models (classification, NLP, computer vision, recommender systems, etc.). - Ensure production-grade model performance, latency, accuracy, and interpretability. - Integrate AI models into existing systems using MLOps best practices. Technical Expertise: - Architect end-to-end ML pipelines: data ingestion, preprocessing, model training, evaluation, and monitoring. - Apply modern AI frameworks such as TensorFlow, PyTorch, Hugging Face Transformers, LangChain, etc. - Utilize cloud platforms (AWS/GCP/Azure) for scalable AI deployments. Team Management & Mentorship: - Mentor and guide junior data scientists and engineers. - Conduct code reviews, knowledge sharing, and promote AI best practices. - Build a strong AI culture within the team. Stakeholder Engagement: - Collaborate with product, engineering, and business teams to translate requirements into technical solutions. - Communicate AI concepts and ROI to non-technical stakeholders. Required Skills & Qualifications: - Bachelors or Masters in Computer Science, AI/ML, Data Science, or related field. - 8-10 years of experience in data science, machine learning, or AI-focused roles. - Proven experience leading AI projects from design to deployment. - Proficient in Python, SQL, ML libraries (Scikit-learn, XGBoost, LightGBM), and deep learning frameworks (TensorFlow/PyTorch). - Hands-on experience with GenAI, LLMs, or vector databases is a strong plus. - Experience with Docker, Kubernetes, MLflow, Airflow, or similar tools. - Solid understanding of data governance, AI ethics, and model interpretability. Preferred Skills (Good to Have): - Exposure to LLMs, Prompt Engineering, or Agentic AI Frameworks. - Experience in BFSI, fintech, healthcare, or manufacturing domains. - Knowledge of MLOps tools like Kubeflow, SageMaker, or Vertex AI.,

The ML Solutions team within Markets OPS Technology is dedicated to developing solutions using Artificial Intelligence, Machine Learning, and Generative AI. This team is a leader in creating new ideas, innovative technology solutions, and ground-breaking solutions for Markets Operations and Other Line of Businesses. We work closely with our clients and business partners to progress solutions from ideation to production by leveraging the entrepreneurial spirit and technological excellence. The ML Solutions team is seeking a Data Scientist/Machine Learning Engineer to drive the design, development, and deployment of innovative AI/ML and GenAI-based solutions. In this hands-on role, you will leverage your expertise to create a variety of AI models, guiding a team from initial concept to successful production. A key aspect involves mentoring team members and fostering their growth. You will collaborate closely with business partners and stakeholders, championing the adoption of these advanced technologies to enhance client experiences, deliver tangible value to our customers, and ensure adherence to regulatory requirements through cutting-edge technical solutions. This position offers a unique opportunity to shape the future of our AI initiatives and make a significant impact on the organization. Key Responsibilities: Hands-On Execution and Delivery: Actively contribute to the development and delivery of AI solutions, driving innovation and excellence within the team. Take a hands-on approach to ensure AI models are successfully deployed into production environments, meeting high-quality standards and performance benchmarks. Mentoring Young Talents: Mentoring team, guiding data analysts/ML engineers from concept to production. This involves fostering technical growth, providing project oversight, and ensuring adherence to best practices, ultimately building a high-performing and innovative team. Quality Control: Ensure the quality and performance of generative AI models, conducting rigorous testing and evaluation. Research and Development: Participate in research activities to explore and advance state-of-the-art generative AI techniques. Stay actively engaged in monitoring ongoing research efforts, keeping abreast of emerging trends, and ensuring that the Generative AI team remains at the forefront of the field. Cross-Functional Collaboration: Collaborate effectively with various teams, including product managers, engineers, and data scientists, to integrate AI technologies into products and services. Skills & Qualifications: - 8 to 13 years of Strong hands-on experience in Machine Learning, delivering complex solutions to production. - Experience with Generative AI technologies essential. - Understanding of concepts like supervised, unsupervised, clustering, embedding. - Knowledge of NLP, Name Entity Recognition, Computer Vision, Transformers, Large Language Models. - In-depth knowledge of deep learning and Generative AI frameworks such as, Langchain, Lang Graph, Crew AI or similar. - Experience with and other open-source frameworks/ libraries/ APIs like Hugging Face Transformers, Spacy, Pandas, scikit-learn, NumPy, OpenCV. - Experience in using Machine Learning/Deep Learning: XGBoost, LightGBM, TensorFlow, PyTorch, Keras. - Proficiency in Python Software Development, following Object-Oriented design patterns and best practices. - Strong background in mathematics: linear algebra, probability, statistics, and optimization. - Experience with evaluation, scoring with framework like ML Flow. - Experience of Docker container and edited a Docker file, experience with K8s is a plus. - Experience with Postgres and Vector DBs a plus. - Excellent problem-solving skills and the ability to think creatively. - Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams. - Publications and contributions to the AI research community are a plus. - Masters degree/Ph. D. or equivalent experience in Computer Science, Data Science, Statistics, or a related field. - 8-12 years of experience This job description provides a high-level review of the types of work performed. Other job-related duties may be assigned as required.,

???? Were Hiring: AI Engineer ???? Location: Bengaluru (Regular office-based role) ???? Employment Type: Full-Time | 5 Days a Week from Office ???? Qualification: B.E / B.Tech or equivalent degree in Computer Science, IT, or related field Are you passionate about building and scaling production-grade AI/ML systems We&aposre looking for a skilled AI Engineer to join our team in Bengaluru and help drive real-world impact through cutting-edge machine learning and GenAI technologies. ???? Must-Have Skills: 48 years of hands-on experience in designing, building, and deploying production-grade AI/ML solutions Proficiency in Python, PySpark, SQL , and ML libraries like Scikit-learn, XGBoost, LightGBM Cloud-native ML development experience on AWS (SageMaker), GCP (Vertex AI), or Azure ML Strong background in NLP/GenAI frameworks: Hugging Face, LangChain, LlamaIndex Practical knowledge of MLOps tools (MLflow, Weights & Biases, DVC) and deployment frameworks ( FastAPI, Flask, Docker, Kubernetes ) Excellent communication, stakeholder engagement, and team collaboration skills Good-to-Have: Publications, blog posts, or open-source contributions in AI/ML Experience leading AI strategy or owning technical roadmaps Show more Show less

House of Shipping is seeking a high-caliber Data Science Lead to join their team in Hyderabad. With a background of 15-18 years in data science, including at least 5 years in leadership roles, the ideal candidate will have a proven track record in building and scaling data science teams in logistics, e-commerce, or manufacturing. Strong expertise in statistical learning, ML architecture, productionizing models, and impact tracking is essential for this role. As the Data Science Lead, you will be responsible for leading enterprise-scale data science initiatives in supply chain optimization, forecasting, network analytics, and predictive maintenance. This position requires a blend of technical leadership and strategic alignment with various business units to deliver measurable business impact. Key responsibilities include defining and driving the data science roadmap across forecasting, route optimization, warehouse simulation, inventory management, and fraud detection. You will work closely with engineering teams to architect end-to-end pipelines, from data ingestion to API deployment. Proficiency in Python and MLOps tools like Scikit-Learn, XGBoost, PyTorch, MLflow, Vertex AI, or AWS SageMaker is crucial for success in this role. Collaboration with operations, product, and technology teams to prioritize AI use cases and define business metrics will be a key aspect of the job. Additionally, you will be responsible for managing experimentation frameworks, mentoring team members, ensuring model validation, and contributing to organizational data maturity. The ideal candidate will possess a Bachelor's, Master's, or Ph.D. degree in Computer Science, Mathematics, Statistics, or Operations Research. Certifications in Cloud ML stacks, MLOps, or Applied AI are preferred. To excel in this role, you should have a strategic vision in AI applications across the supply chain, strong team mentorship skills, expertise in statistical and ML frameworks, and experience in MLOps pipeline management. Excellent business alignment and executive communication skills are also essential for this position. If you are a data science leader looking to make a significant impact in the logistics industry, we encourage you to apply for this exciting opportunity with House of Shipping.,

Aarki is an AI company dedicated to developing advertising solutions that drive mobile revenue growth. Utilizing AI technology, we identify audiences in a privacy-first environment by analyzing trillions of contextual bidding signals alongside our proprietary behavioral models. Our comprehensive audience engagement platform consists of a full-service agency team and a Unified Creative Strategy that offers ad creative ideation and execution services. With a history of working with numerous advertisers over 14 years, we process 5 million mobile ad requests per second from over 10 billion devices, effectively enhancing performance for publishers and brands. Headquartered in San Francisco, CA, we also maintain offices in the United States, EMEA, and APAC. We are currently looking for a dynamic and detail-oriented Senior Manager of Applied Data Science to join our team. As a Senior Manager, you will lead a team responsible for creating and implementing machine learning models and data pipelines to optimize our programmatic demand-side platform (DSP). Collaborating closely with decision science, engineering, and analytics teams, you will drive impactful data science projects and contribute to innovative solutions. Join us in our mission to push the boundaries of AI and mobile advertising within a collaborative environment that encourages creativity and growth. At Aarki, we offer a competitive salary, comprehensive benefits, and substantial opportunities for career advancement. Responsibilities include: - Building, nurturing, and mentoring a high-performing data science team, emphasizing technical growth and long-term career development. - Spearheading the development of scalable machine learning pipelines tailored for programmatic mobile advertising. - Establishing best practices for model deployment, monitoring, and performance assessment. - Taking ownership of process enhancements throughout the ML lifecycle. - Promoting collaboration with teams in Bangalore and San Francisco, including decision science and bidder engineering, to deploy models into production and conduct experiments. - Ensuring timely project deliveries and swift resolution of model-related issues in production environments. - Designing and managing data pipelines to facilitate the efficient processing of large-scale datasets for training and evaluation. - Collaborating with product managers, engineers, and business stakeholders to seamlessly integrate models into production systems. - Staying abreast of advancements in machine learning and applying cutting-edge techniques to enhance platform capabilities. - Driving innovation by exploring and adopting new methodologies, tools, and technologies. The ideal candidate possesses: - Over 7 years of hands-on experience in data science, with a solid background in machine learning, statistics, and modeling. - More than 3 years of experience in managing teams of data scientists or ML engineers. - Proficiency in Python and SQL, with familiarity in Spark being advantageous. - Expertise in ML libraries like Scikit-Learn, LightGBM, TensorFlow, and PyTorch. - Profound understanding of probability, statistical analysis, and experimental design. - Demonstrated expertise in deploying ML models into production and ensuring continuous model observability. - Exceptional communication skills enabling the effective explanation of complex concepts to both technical and non-technical audiences. - Knowledge of RTB, auction theory, and high-throughput low-latency environments is a plus.,

The ML Solutions team within Markets OPS Technology is dedicated to developing solutions using Artificial Intelligence, Machine Learning, and Generative AI. This team is a leader in creating new ideas, innovative technology solutions, and ground-breaking solutions for Markets Operations and Other Line of Businesses. We work closely with our clients and business partners to progress solutions from ideation to production by leveraging the entrepreneurial spirit and technological excellence. The ML Solutions team is seeking a Data Scientist/Machine Learning Engineer to drive the design, development, and deployment of innovative AI/ML and GenAI-based solutions. In this hands-on role, you will leverage your expertise to create a variety of AI models, guiding a team from initial concept to successful production. A key aspect involves mentoring team members and fostering their growth. You will collaborate closely with business partners and stakeholders, championing the adoption of these advanced technologies to enhance client experiences, deliver tangible value to our customers, and ensure adherence to regulatory requirements through cutting-edge technical solutions. This position offers a unique opportunity to shape the future of our AI initiatives and make a significant impact on the organization. Key Responsibilities: - Hands-On Execution and Delivery: Actively contribute to the development and delivery of AI solutions, driving innovation and excellence within the team. Take a hands-on approach to ensure AI models are successfully deployed into production environments, meeting high-quality standards and performance benchmarks. - Mentoring Young Talents: Mentoring team, guiding data analysts/ML engineers from concept to production. This involves fostering technical growth, providing project oversight, and ensuring adherence to best practices, ultimately building a high-performing and innovative team. - Quality Control: Ensure the quality and performance of generative AI models, conducting rigorous testing and evaluation. - Research and Development: Participate in research activities to explore and advance state-of-the-art generative AI techniques. Stay actively engaged in monitoring ongoing research efforts, keeping abreast of emerging trends, and ensuring that the Generative AI team remains at the forefront of the field. - Cross-Functional Collaboration: Collaborate effectively with various teams, including product managers, engineers, and data scientists, to integrate AI technologies into products and services. Skills & Qualifications: - 8 to 13 years of Strong hands-on experience in Machine Learning, delivering complex solutions to production. - Experience with Generative AI technologies essential. - Understanding of concepts like supervised, unsupervised, clustering, embedding. - Knowledge of NLP, Name Entity Recognition, Computer Vision, Transformers, Large Language Models. - In-depth knowledge of deep learning and Generative AI frameworks such as, Langchain, Lang Graph, Crew AI or similar. - Experience with and other open-source frameworks/ libraries/ APIs like Hugging Face Transformers, Spacy, Pandas, scikit-learn, NumPy, OpenCV. - Experience in using Machine Learning/Deep Learning: XGBoost, LightGBM, TensorFlow, PyTorch, Keras. - Proficiency in Python Software Development, following Object-Oriented design patterns and best practices. - Strong background in mathematics: linear algebra, probability, statistics, and optimization. - Experience with evaluation, scoring with a framework like ML Flow - Experience of Docker container and edited a Docker file, experience with K8s is a plus. - Experience with Postgres and Vector DBs a plus. - Excellent problem-solving skills and the ability to think creatively. - Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams - Publications and contributions to the AI research community are a plus. - Masters degree/Ph. D. or equivalent experience in Computer Science, Data Science, Statistics, or a related field. - 8-12 years of experience This job description provides a high-level review of the types of work performed. Other job-related duties may be assigned as required.,

As a Senior Data Scientist specializing in Generative AI at our organization, you will play a crucial role in driving technical innovation and delivering impactful results within a team setting. You will be responsible for working with financial data, applying NLP techniques, refining prompt engineering strategies for LLMs, collaborating with stakeholders, developing and testing Python code for GenAI solutions, integrating with vector databases, monitoring MLOps pipelines, researching emerging GenAI technologies, and troubleshooting and debugging GenAI models in production. To excel in this role, you must possess a Master's degree in Computer Science, Data Science, or a related field, with a Ph.D. being preferred. You should have 8-12 years of experience in a Data Scientist or equivalent role, with at least 4 years of specialized experience in Generative AI. Additionally, you should have demonstrable experience in the full lifecycle of real-world, production-level GenAI project implementation. You must stay up-to-date with the rapidly evolving GenAI landscape, continuously learn and explore new tools and techniques, and effectively communicate technical concepts to non-technical stakeholders. Proficiency in Python, along with expertise in key libraries for AI/ML and GenAI applications such as TensorFlow, PyTorch, and Transformers, is mandatory. Experience in designing and implementing scalable API architectures for GenAI applications, along with a deep understanding of Machine Learning concepts, is essential. Preferred qualifications for this role include experience with MLOps, cloud computing platforms, conversational AI solutions, and contributions to research and publications in the field of Generative AI. As a Senior Data Scientist, you will be expected to demonstrate strong technical leadership and mentorship skills, as well as excellent communication and collaboration abilities. This job description provides a comprehensive overview of the responsibilities and qualifications required for the Senior Data Scientist position. If you are a highly motivated individual with a strong passion for Generative AI and possess the necessary technical skills and experience, we encourage you to apply for this role.,

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.,

Job Summary: We are looking for a highly skilled and experienced AI/ML Developer-Lead with 4-5 years of hands-on relevant experience to join our technology team. You will be responsible for designing, developing, and optimizing machine learning models that drive intelligent business solutions. The role involves close collaboration with cross-functional teams to deploy scalable AI systems and stay abreast of evolving trends in artificial intelligence and machine learning. Key Responsibilities: 1. Develop and Implement AI/ML Models Design, build, and implement AI/ML models tailored to solve specific business challenges, including but not limited to natural language processing (NLP), image recognition, recommendation systems, and predictive analytics. 2. Model Optimisation and Evaluation Continuously improve existing models for performance, accuracy, and scalability. 3. Data Preprocessing and Feature Engineering Collect, clean, and preprocess structured and unstructured data from various sources. Engineer relevant features to improve model performance and interpret ability. 4. Collaboration and Communication Collaborate closely with data scientists, back end engineers, product managers, and stakeholders to align model development with business goals. Communicate technical insights clearly to both technical and non-technical stakeholders. 5. Model Deployment and Monitoring Deploy models to production using MLOps practices and tools (e.g., MLflow, Docker, Kubernetes). Monitor live model performance, diagnose issues, and implement improvements as needed. 6. Staying Current with AI/ML Advancements Stay informed of current research, tools, and trends in AI and machine learning. Evaluate and recommend emerging technologies to maintain innovation within the team. 7. Code Reviews and Best Practices Participate in code reviews to ensure code quality, scalability, and adherence to best practices. Promote knowledge sharing and mentoring within the development team. Required Skills and Qualifications: Bachelors or Masters degree in computer science, Data Science, Engineering, or a related field. 4-5 years' of experience in machine learning, artificial intelligence, or applied data science roles. Strong programming skills in Python (preferred) and/or R. Proficiency in ML libraries and frameworks , including: scikit-learn, XGBoost, LightGBM, TensorFlow or Keras, PyTorch Skilled in data preprocessing and feature engineering , using; pandas, numpy, sklearn.preprocessing Practical experience in deploying ML models into production environments using REST APIs and containers. Familiarity with version control systems (e.g., Git) and containerization tools (e.g., Docker). Experience working with cloud platforms such as AWS, Google Cloud Platform (GCP), or Azure . Understanding software development methodologies , especially Agile/Scrum. Strong analytical thinking, debugging, and problem-solving skills in real-world AI/ML applications.

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.