75 Ml Pipelines Jobs - Page 2

Role and Responsibilities Design, develop, implement, and maintain AIML products to solve specific business problems Optimize the existing codes using advanced programming skills Execute AIML POC projects Collaborate with various stake holders (Data engineers, Cloud Engineers, Bas) in the practice to deliver optimal outcomes Work on various cloud platforms includes AWS, AZURE and GCP Work on big-data tools (PySpark) Design and develop ML pipelines includes feature engineering, model training & testing, model health monitoring and deployments Create reports, projections, models, and presentations to support business strategy and tactics Exercise independent judgment and decision making on complex issues regarding job duties and related tasks, and work under minimal supervision, use independent judgment requiring analysis of variable factors and determining the best course of action Qualifications and Education Requirements Master's Degree (Data Science/statistics/ Mathematics/Computer Science) + 8years of experience, or equivalent experience with a Bachelors degree Proficiency in programming, preferably Python, R, and SQL (PostgreSQL MySQL/) and NoSQL (MongoDB) Experience of Advanced programming in python/R/Spark includes developing multi-threading and Parallel Processing Experience in designing, developing, implementing and deploying ML products Experience in using mathematics, statistics, modeling, business analysis, and technology to transform high volumes of complex data into advanced analytic solutions Demonstrated experience in AI Implementation and ML modeling problems of diverse scope and complexity Experience in Supervised and Unsupervised ML models training, testing and Validations Experience in developing, maintaining, and collecting structured and unstructured data sets for analysis and reporting Experience in creating reports, projections, models, and presentations to support business Ability to exercise independent judgment and decision making on complex issues regarding job duties and related tasks Ability to works under minimal supervision, using independent judgment Experience of working in agile & CICD environment Preferred Skills PhD in Math/Stat/Computer science/Data Science/Engineering Previous experience in IOT industry Experience in leading/ mentoring Experience in Hadoop Experience in Kafka Streaming.

Role and Responsibilities Design, develop, implement, and maintain AIML products to solve specific business problems Optimize the existing codes using advanced programming skills Execute AIML POC projects Collaborate with various stake holders (Data engineers, Cloud Engineers, Bas) in the practice to deliver optimal outcomes Work on various cloud platforms includes AWS, AZURE and GCP Work on big-data tools (PySpark) Design and develop ML pipelines includes feature engineering, model training & testing, model health monitoring and deployments Create reports, projections, models, and presentations to support business strategy and tactics Exercise independent judgment and decision making on complex issues regarding job duties and related tasks, and work under minimal supervision, use independent judgment requiring analysis of variable factors and determining the best course of action Qualifications and Education Requirements Master's Degree (Data Science/statistics/Mathematics/Computer Science) + 8years of experience, or equivalent experience with a Bachelor s degree Proficiency in programming, preferably Python, R, and SQL (PostgreSQL MySQL/) and NoSQL (MongoDB) Experience of Advanced programming in python/R/Spark includes developing multi-threading and Parallel Processing Experience in designing, developing, implementing and deploying ML products Experience in using mathematics, statistics, modeling, business analysis, and technology to transform high volumes of complex data into advanced analytic solutions Demonstrated experience in AI Implementation and ML modeling problems of diverse scope and complexity Experience in Supervised and Unsupervised ML models training, testing and Validations Experience in developing, maintaining, and collecting structured and unstructured data sets for analysis and reporting Experience in creating reports, projections, models, and presentations to support business Ability to exercise independent judgment and decision making on complex issues regarding job duties and related tasks Ability to works under minimal supervision, using independent judgment Experience of working in agile & CICD environment Preferred Skills PhD in Math/Stat/Computer science/Data Science/Engineering Previous experience in IOT industry Experience in leading/ mentoring Experience in Hadoop Experience in Kafka Streaming

We are looking for a Machine Learning Engineer with expertise in MLOps (Machine Learning Operations) or LLMOps (Large Language Model Operations) to design, deploy, and maintain scalable AI/ML systems. You will work on automating ML workflows, optimizing model deployment, and managing large-scale AI applications, including LLMs (Large Language Models) , ensuring they run efficiently in production. Key Responsibilities: Design and implement end-to-end MLOps pipelines for training, validation, deployment, monitoring, and retraining of ML models. Optimize and fine-tune large language models (LLMs) for various applications, ensuring performance and efficiency. Develop CI/CD pipelines for ML models to automate deployment and monitoring in production. Monitor model performance, detect drift , and implement automated retraining mechanisms. Work with cloud platforms ( AWS, GCP, Azure ) and containerization technologies ( Docker, Kubernetes ) for scalable deployments. Implement best practices in data engineering , feature stores, and model versioning. Collaborate with data scientists, engineers, and product teams to integrate ML models into production applications. Ensure compliance with security, privacy, and ethical AI standards in ML deployments. Optimize inference performance and cost of LLMs using quantization, pruning, and distillation techniques . Deploy LLM-based APIs and services, integrating them with real-time and batch processing pipelines. Key Requirements: Technical Skills: Strong programming skills in Python, with experience in ML frameworks ( TensorFlow, PyTorch, Hugging Face, JAX ). Experience with MLOps tools (MLflow, Kubeflow, Vertex AI, SageMaker, Airflow). Deep understanding of LLM architectures , prompt engineering, and fine-tuning. Hands-on experience with containerization (Docker, Kubernetes) and orchestration tools . Proficiency in cloud services (AWS/GCP/Azure) for ML model training and deployment. Experience with monitoring ML models (Prometheus, Grafana, Evidently AI). Knowledge of feature stores (Feast, Tecton) and data pipelines (Kafka, Apache Beam). Strong background in distributed computing (Spark, Ray, Dask) . Soft Skills: Strong problem-solving and debugging skills. Ability to work in cross-functional teams and communicate complex ML concepts to stakeholders. Passion for staying updated with the latest ML and LLM research & technologies . Preferred Qualifications: Experience with LLM fine-tuning , Reinforcement Learning with Human Feedback ( RLHF ), or LoRA/PEFT techniques . Knowledge of vector databases (FAISS, Pinecone, Weaviate) for retrieval-augmented generation ( RAG ). Familiarity with LangChain, LlamaIndex , and other LLMOps-specific frameworks. Experience deploying LLMs in production (ChatGPT, LLaMA, Falcon, Mistral, Claude, etc.) .

Expected Notice Period : 15 Days Shift : (GMT+05:30) Asia/Kolkata (IST) Opportunity Type : Remote (*Note: This is a requirement for one of Uplers' client - A fast-growing, VC-backed B2B SaaS platform revolutionizing financial planning and analysis for modern finance teams.) What do you need for this opportunity? Must have skills required: async workflows, MLOps, Ray Tune, Data Engineering, MLFlow, Supervised Learning, Time-Series Forecasting, Docker, machine_learning, NLP, Python, SQL A fast-growing, VC-backed B2B SaaS platform revolutionizing financial planning and analysis for modern finance teams. is Looking for: We are a fast-moving startup building AI-driven solutions to the financial planning workflow. Were looking for a versatile Machine Learning Engineer to join our team and take ownership of building, deploying, and scaling intelligent systems that power our core product. Job Description- Full-time Team: Data & ML Engineering Were looking for 5+ years of experience as a Machine Learning or Data Engineer (startup experience is a plus) WHAT YOU WILL DO- Build and optimize machine learning models from regression to time-series forecasting Work with data pipelines and orchestrate training/inference jobs using Ray, Airflow, and Docker Train, tune, and evaluate models using tools like Ray Tune, MLflow, and scikit-learn Design and deploy LLM-powered features and workflows Collaborate closely with product managers to turn ideas into experiments and production-ready solutions Partner with Software and DevOps engineers to build robust ML pipelines and integrate them with the broader platform BASIC SKILLS Proven ability to work creatively and analytically in a problem-solving environment Excellent communication (written and oral) and interpersonal skills Strong understanding of supervised learning and time-series modeling Experience deploying ML models and building automated training/inference pipelines Ability to work cross-functionally in a collaborative and fast-paced environment Comfortable wearing many hats and owning projects end-to-end Write clean, tested, and scalable Python and SQL code Leverage async workflows and cloud-native infrastructure (S3, Docker, etc.) for high-throughput data processing. ADVANCED SKILLS Familiarity with MLOps best practices Prior experience with LLM-based features or production-level NLP Experience with LLMs, vector stores, or prompt engineering Contributions to open-source ML or data tools TECH STACK Languages: Python, SQL Frameworks & Tools: scikit-learn, Prophet, pyts, MLflow, Ray, Ray Tune, Jupyter Infra: Docker, Airflow, S3, asyncio, Pydantic

About the Role: Grade Level (for internal use): 12 The Team As a member of the EDO, Collection Platforms & AI Cognitive Engineering team you will spearhead the design and delivery of robust, scalable ML infrastructure and pipelines that power natural language understanding, data extraction, information retrieval, and data sourcing solutions for S&P Global. You will define AI/ML engineering best practices, mentor fellow engineers and data scientists, and drive production-ready AI products from ideation through deployment. Youll thrive in a (truly) global team that values thoughtful risk-taking and self-initiative. Whats in it for you Be part of a global company and build solutions at enterprise scale Lead and grow a technically strong ML engineering function Collaborate on and solve high-complexity, high-impact problems Shape the engineering roadmap for emerging AI/ML capabilities (including GenAI integrations) Key Responsibilities Architect, develop, and maintain production-ready data acquisition, transformation, and ML pipelines (batch & streaming) Serve as a hands-on lead-writing code, conducting reviews, and troubleshooting to extend and operate our data platforms Apply best practices in data modeling, ETL design, and pipeline orchestration using cloud-native solutions Establish CI/CD and MLOps workflows for model training, validation, deployment, monitoring, and rollback Integrate GenAI components-LLM inference endpoints, embedding stores, prompt services-into broader ML systems Mentor and guide engineers and data scientists; foster a culture of craftsmanship and continuous improvement Collaborate with cross-functional stakeholders (Data Science, Product, IT) to align on requirements, timelines, and SLAs What Were Looking For 8-12 years' professional software engineering experience with a strong MLOps focus Expert in Python and Apache for large-scale data processing Deep experience deploying and operating ML pipelines on AWS or GCP Hands-on proficiency with container/orchestration tooling Solid understanding of the full ML model lifecycle and CI/CD principles Skilled in streaming and batch ETL design (e.g., Airflow, Dataflow) Strong OOP design patterns, Test-Driven Development, and enterprise system architecture Advanced SQL skills (big-data variants a plus) and comfort with Linux/bash toolsets Familiarity with version control (Git, GitHub, or Azure DevOps) and code review processes Excellent problem-solving, debugging, and performance-tuning abilities Ability to communicate technical change clearly to non-technical audiences Nice to have Redis, Celery, SQS and Lambda based event driven pipelines Prior work integrating LLM services (OpenAI, Anthropic, etc.) at scale Experience with Apache Avro and Apache Familiarity with Java and/or .NET Core (C#) Whats In It For You Our Purpose: Progress is not a self-starter. It requires a catalyst to be set in motion. Information, imagination, people, technologythe right combination can unlock possibility and change the world.Our world is in transition and getting more complex by the day. We push past expected observations and seek out new levels of understanding so that we can help companies, governments and individuals make an impact on tomorrow. At S&P Global we transform data into Essential Intelligence, pinpointing risks and opening possibilities. We Accelerate Progress. Our People: Our Values: Integrity, Discovery, Partnership At S&P Global, we focus on Powering Global Markets. Throughout our history, the world's leading organizations have relied on us for the Essential Intelligence they need to make confident decisions about the road ahead. We start with a foundation of integrity in all we do, bring a spirit of discovery to our work, and collaborate in close partnership with each other and our customers to achieve shared goals. Benefits: We take care of you, so you cantake care of business. We care about our people. Thats why we provide everything youand your careerneed to thrive at S&P Global. Health & WellnessHealth care coverage designed for the mind and body. Continuous LearningAccess a wealth of resources to grow your career and learn valuable new skills. Invest in Your FutureSecure your financial future through competitive pay, retirement planning, a continuing education program with a company-matched student loan contribution, and financial wellness programs. Family Friendly PerksIts not just about you. S&P Global has perks for your partners and little ones, too, with some best-in class benefits for families. Beyond the BasicsFrom retail discounts to referral incentive awardssmall perks can make a big difference. For more information on benefits by country visithttps://spgbenefits.com/benefit-summaries Global Hiring and Opportunity at S&P Global: At S&P Global, we are committed to fostering a connected andengaged workplace where all individuals have access to opportunities based on their skills, experience, and contributions. Our hiring practices emphasize fairness, transparency, and merit, ensuring that we attract and retain top talent. By valuing different perspectives and promoting a culture of respect and collaboration, we drive innovation and power global markets. Recruitment Fraud Alert If you receive an email from a spglobalind.com domain or any other regionally based domains, it is a scam and should be reported to reportfraud@spglobal.com. S&P Global never requires any candidate to pay money for job applications, interviews, offer letters, pre-employment training or for equipment/delivery of equipment. Stay informed and protect yourself from recruitment fraud by reviewing our guidelines, fraudulent domains, and how to report suspicious activity here. ----------------------------------------------------------- Equal Opportunity Employer S&P Global is an equal opportunity employer and all qualified candidates will receive consideration for employment without regard to race/ethnicity, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, marital status, military veteran status, unemployment status, or any other status protected by law. Only electronic job submissions will be considered for employment. If you need an accommodation during the application process due to a disability, please send an email to EEO.Compliance@spglobal.com and your request will be forwarded to the appropriate person. US Candidates Only The EEO is the Law Poster http://www.dol.gov/ofccp/regs/compliance/posters/pdf/eeopost.pdf describes discrimination protections under federal law. Pay Transparency Nondiscrimination Provision - https://www.dol.gov/sites/dolgov/files/ofccp/pdf/pay-transp_%20English_formattedESQA508c.pdf ----------------------------------------------------------- IFTECH103.2 - Middle Management Tier II (EEO Job Group)

Role: Junior AI/ML Engineer Location: Ambattur, Chennai(Onsite) Fulltime Position Job Summary: We are looking for an AI/ML Engineer to develop, optimize, and deploy machine learning models for real-world applications. You will work on end-to-end ML pipelines , collaborate with cross-functional teams, and apply AI techniques such as NLP, Computer Vision, and Time-Series Forecasting . This role offers opportunities to work on cutting-edge AI solutions while growing your expertise in model deployment and optimization. Key Responsibilities: Design, build, and optimize machine learning models for various business applications. Develop and maintain ML pipelines , including data preprocessing, feature engineering, and model training. Work with TensorFlow, PyTorch, Scikit-learn, and Keras for model development. Deploy ML models in cloud environments (AWS, Azure, GCP) and work with Docker/Kubernetes for containerization. Perform model evaluation, hyperparameter tuning, and performance optimization . Collaborate with data scientists, engineers, and product teams to deliver AI-driven solutions. Stay up to date with the latest advancements in AI/ML and implement best practices. Write clean, scalable, and well-documented code in Python or R. Technical Skills: Programming Languages: Proficiency in languages like Python. Python is particularly popular for developing ML models and AI algorithms due to its simplicity and extensive libraries like NumPy, Pandas, and Scikit-learn. Machine Learning Algorithms: Should have a deep understanding of supervised learning (linear regression, decision trees, SVM), unsupervised learning, and reinforcement learning. Data Management and Analysis: Skills in data cleaning, feature engineering, and data transformation are crucial. Deep Learning: Familiarity with neural networks, CNNs, RNNs, and other architectures is important. Machine Learning Frameworks and Libraries: Experience with TensorFlow, PyTorch, Keras, or Scikit-learn is valuable. Natural Language Processing (NLP): Familiarity with NLP techniques like word2vec, sentiment analysis, and summarization can be beneficial. Cloud Computing: Experience with cloud-based services like AWS SageMaker, Google Cloud AI Platform, or Microsoft Azure Machine Learning. Data Preprocessing: Skills in handling missing data, data normalization, feature scaling, and data transformation. Feature Engineering: Ability to create new features from existing data to improve model performance. Data Visualization: Familiarity with visualization tools like Matplotlib, Seaborn, Plotly, or Tableau. Containerization: Knowledge of containerization tools like Docker and Kubernetes. Databases : Understanding of relational databases (e.g., MySQL) and NoSQL databases (e.g., MongoDB). Data Warehousing: Familiarity with data warehousing concepts and tools like Amazon Redshift or Google BigQuery. Computer Vision: Understanding of computer vision concepts and techniques like object detection, segmentation, and image classification.

Your Role and Responsibilities 3+ years of experience in a Data Science, machine learning or a related field. Strong hands-on experience in Machine Learning and Statistics focusing on structured and unstructured data problems. Practical experience in several of the following areas: time series forecasting, clustering and classification techniques, regression, boosting algorithms, optimization techniques, NLP, recommendation systems, ElasticNet Excellent programming skills preferably in Python/Py spark and SQL Understanding of developing, implementing, deploying machine learning models on the cloud platforms(Azure, AWS, GCP) by using AWS/Azure Machine Learning, Data bricks, or other relevant cloud services Integrate machine learning models into existing systems and applications, ensuring seamless functionality and data flow Understanding of developing and maintaining MLOps pipelines for automated model training, testing, deployment, and monitoring Understanding of monitoring and analysing model performance, providing reports and insights to stakeholders as needed Familiarity with data processing and storage tools, such as SQL, Hadoop, or Spark Advanced engineering abilities to deliver flexible and scalable end-to-end machine learning solutions. Exposure to data visualization software and packages (Power BI, Tableau, matplotlib, d3) Understands challenges in business area, applicability of relevant data science disciplines, and system interactions. Excellent written and verbal communication skills, confidence in presenting ideas and findings to stakeholders, and ability to do so at the right level of detail. Required Technical and Professional Expertise Engineering Graduate from a reputed institute and/or Masters in Statistics, MBA 3+ years of Data science experience Strong expertise and deep understanding of machine learning. Strong understanding of SQL & Python. Knowledge of Power BI or Tableau is a plus Exposure to Industry specific (CPG, Manufacturing) use cases is required Strong client-facing skills Must be organized and detail oriented. Excellent communication and interpersonal skills Preferred Technical and Professional Experience Strong foundation in Supervised and Unsupervised Learning (Regression, Classification, Clustering, etc.). Proficiency in Ensemble Learning (Random Forest, Gradient Boosting, XGBoost, LightGBM, etc.). Experience in fine-tuning Large Language Models (LLMs) and working with open-source models (Llama, GPT, BERT, etc.). Familiarity with Prompt Engineering, RAG (Retrieval-Augmented Generation), and Fine-tuning techniques. Hands-on experience with Cloud Platforms (AWS, GCP, Azure) for ML model deployment. Familiarity with MLOps and Model Deployment using Kubernetes, Docker, and MLflow.

Machine Learning Engineer About Caliper: Caliper is an AI-enabled, comprehensive value-based care (VBC) risk analytics suite designed to provide affordable, accessible and actionable insights. Our solution empowers Community Care Providers to thrive in the VBC landscape, ensuring improved patient outcomes, operational efficiency and financial success. Position: Machine Learning Engineer (with NLP & AWS Experience) We are hiring a Machine Learning Engineer who brings strong foundational skills across ML workflows, with a working focus on Natural Language Processing (NLP) and experience deploying ML systems on AWS. This role is ideal for someone who is technically versatilecomfortable working across diverse machine learning problems including NLP, classification, forecasting, embeddings, and recommender systemswhile being hands-on with modern ML tooling, model lifecycle management, and cloud infrastructure. Key Responsibilities: Build and deploy ML models for a variety of use cases such as classification, prediction, NLP tasks, and recommender systems. Design, implement, and maintain end-to-end ML pipelines, including data preprocessing, model training, validation, and deployment. Apply NLP techniques where applicable (e.g., sentiment analysis, NER, document parsing, embeddings). Leverage AWS services (e.g., SageMaker, Lambda, S3, Bedrock, Comprehend) to deploy and scale ML solutions in production. Participate in model evaluation, monitoring, and retraining workflows. Collaborate with product, data, and engineering teams to understand requirements and translate them into ML-driven solutions. Support both experimental research and production-grade deployment workstreams. Required Skills & Experience: 4+ years of experience as a Machine Learning Engineer or Applied Scientist. Strong hands-on experience in core ML techniques: regression, classification, clustering, tree-based models, embeddings, etc. Solid Python programming skills and experience with libraries like Scikit-learn, PyTorch or TensorFlow, Pandas, NumPy. Exposure to NLP models and libraries (e.g., Hugging Face Transformers, spaCy, NLTK) with practical application experience. Experience deploying models using AWS cloud infrastructure, particularly SageMaker, Comprehend, Lambda, or Bedrock. Comfortable with model evaluation, metrics (e.g., accuracy, ROC-AUC, F1), and debugging pipelines in production. Experience working with version control, CI/CD tools, and basic MLOps practices. Nice to Have: Familiarity with Retrieval-Augmented Generation (RAG) pipelines and vector databases (e.g., FAISS, Milvus, Weaviate). Knowledge of prompt engineering or foundation model tuning (e.g., OpenAI, Claude, Bedrock). Experience with time series models, anomaly detection, or customer intelligence use cases. Exposure to Docker, Kubernetes, or Airflow for workflow orchestration. What We are Looking For: A generalist ML engineer who can adapt to evolving problem statements across NLP, tabular, or other ML use cases. Someone who balances code quality and experimentation, and can own model delivery end-to-end. A collaborative team player who is curious, self-driven, and excited to build in a fast-paced environment. Compensation will be commensurate with experience. If you are interested, please send your application to jobs@precognitas.com and For more information about our work, visit www.caliper.care

IMMEDIATE JOINERS ONLY Job Title: Senior MLOps Engineer Location : NCR Location (WFO) Note: DevOps -Knowledge is fine Experience Range: 6-12 years Primary Key skills: MLOps Key Responsibilities: Design, develop and maintain end-to-end MLOps pipelines for model deployment, monitoring, maintenance, and scalability. Automate the retraining, testing, and validation processes for ML models. Collaborate with cross-functional teams, including data science, software engineering and DevOps, to integrate ML models into production systems. Monitor model performance, diagnose issues, and implement improvements. Ensure scalability, reliability, and compliance of ML systems in production. Optimize infrastructure costs while maintaining high system performance. Stay up-to-date with the latest developments in MLOps, machine learning and AI, and apply new techniques to improve existing models and processes. Qualifications: Education: Bachelors or Master’s degree in Computer Science, Data Science, Statistics, Applied Mathematics, or a related field. Experience: Solid experience as a MLOps Engineer, Machine Learning Engineer, DevOps Engineer, or similar role. Experience in the retail industry or e-commerce is highly desirable. Technical Skills: Strong experience with Infrastructure as Code frameworks and languages (Terraform, Bicep or ARM) Strong programming skills in Python and experience with ML frameworks (TensorFlow, PyTorch, etc.). Hands-on experience with containerization (Docker) and orchestration tools (Kubernetes). Proficiency in CI/CD tools and cloud platforms (AWS, Azure, or Google Cloud). Knowledge of model monitoring and evaluation metrics. Familiarity with version control systems, such as Git, and model versioning tools like MLflow or DVC. Experience with Generative AI product deployment is desirable. Experience with big data using Databricks, Snowflake, Apache Spark or Hadoop is desirable. – some of these System level architecture understanding including scaling, MLOps, model/data monitoring, andensuring a deterministic pipeline. Soft Skills: Strong problem-solving skills with the ability to work independently and collaboratively in a fast-paced environment. Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders. A proactive attitude and a passion for continuous learning and innovation.

Precognitas Health Pvt. Ltd., a fully owned subsidiary of Foresight Health Solutions LLC, is seeking a Data Engineer to build and optimize our data pipelines, processing frameworks, and analytics infrastructure that power critical healthcare insights. Are you a bright, energetic, and skilled data engineer who wants to make a meaningful impact in a dynamic environment? Do you enjoy designing and implementing scalable data architectures, ML pipelines, automating ETL workflows, and working with cloud-native solutions to process large datasets efficiently? Are you passionate about transforming raw data into actionable insights that drive better healthcare outcomes? If so, join us! Youll play a crucial role in shaping our data strategy, optimizing data ingestion, and ensuring seamless data flow across our systems while leveraging the latest cloud and big data technologies. Required Skills & Experience : 4+ years of experience in data engineering, data pipelines, and ETL/ELT workflows. Strong Python programming skills with expertise in Python Programming, NumPy, Pandas, and data manipulation techniques. Hands-on experience with orchestration tools like Prefect, Apache Airflow, or AWS Step Functions for managing complex workflows. Proficiency in AWS services, including AWS Glue, AWS Batch, S3, Lambda, RDS, Athena, and Redshift. Experience with Docker containerization and Kubernetes for scalable and efficient data processing. Strong understanding of data processing layers, batch and streaming data architectures, and analytics frameworks. Expertise in SQL and NoSQL databases, query optimization, and data modeling for structured and unstructured data. Familiarity with big data technologies like Apache Spark, Hadoop, or similar frameworks. Experience implementing data validation, quality checks, and observability for robust data pipelines. Strong knowledge of Infrastructure as Code (IaC) using Terraform or AWS CDK for managing cloud-based data infrastructure. Ability to work with distributed systems, event-driven architectures (Kafka, Kinesis), and scalable data storage solutions. Experience with CI/CD for data workflows, including version control (Git), automated testing, and deployment pipelines. Knowledge of data security, encryption, and access control best practices in cloud environments. Strong problem-solving skills and ability to collaborate with cross-functional teams, including data scientists and software engineers. Compensation will be commensurate with experience. If you are interested, please send your application to jobs@precognitas.com. For more information about our work, visit www.caliper.care

Role: AI/ML Lead Engineer Location: Ambattur, Chennai(Onsite) Fulltime Position Job Summary: We are looking for an AI/ML Engineer to Lead, develop, optimize, and deploy machine learning models for real-world applications. You will work on end-to-end ML pipelines , collaborate with cross-functional teams, and apply AI techniques such as NLP, Computer Vision, and Time-Series Forecasting . This role offers opportunities to work on cutting-edge AI solutions while growing your expertise in model deployment and optimization. Key Responsibilities: Design, build, and optimize machine learning models for various business applications. Develop and maintain ML pipelines , including data preprocessing, feature engineering, and model training. Work with TensorFlow, PyTorch, Scikit-learn, and Keras for model development. Deploy ML models in cloud environments (AWS, Azure, GCP) and work with Docker/Kubernetes for containerization. Perform model evaluation, hyperparameter tuning, and performance optimization . Collaborate with data scientists, engineers, and product teams to deliver AI-driven solutions. Stay up to date with the latest advancements in AI/ML and implement best practices. Write clean, scalable, and well-documented code in Python or R. Technical Skills: Programming Languages: Proficiency in languages like Python. Python is particularly popular for developing ML models and AI algorithms due to its simplicity and extensive libraries like NumPy, Pandas, and Scikit-learn. Machine Learning Algorithms: Should have a deep understanding of supervised learning (linear regression, decision trees, SVM), unsupervised learning, and reinforcement learning. Data Management and Analysis: Skills in data cleaning, feature engineering, and data transformation are crucial. Deep Learning: Familiarity with neural networks, CNNs, RNNs, and other architectures is important. Machine Learning Frameworks and Libraries: Experience with TensorFlow, PyTorch, Keras, or Scikit-learn is valuable. Natural Language Processing (NLP): Familiarity with NLP techniques like word2vec, sentiment analysis, and summarization can be beneficial. Cloud Computing: Experience with cloud-based services like AWS SageMaker, Google Cloud AI Platform, or Microsoft Azure Machine Learning. Data Preprocessing: Skills in handling missing data, data normalization, feature scaling, and data transformation. Feature Engineering: Ability to create new features from existing data to improve model performance. Data Visualization: Familiarity with visualization tools like Matplotlib, Seaborn, Plotly, or Tableau. Containerization: Knowledge of containerization tools like Docker and Kubernetes. Databases : Understanding of relational databases (e.g., MySQL) and NoSQL databases (e.g., MongoDB). Data Warehousing: Familiarity with data warehousing concepts and tools like Amazon Redshift or Google BigQuery. Computer Vision: Understanding of computer vision concepts and techniques like object detection, segmentation, and image classification.

Role: Midlevel AI/ML Engineer Location: Ambattur, Chennai(Onsite) Fulltime Position Job Summary: We are looking for an AI/ML Engineer to develop, optimize, and deploy machine learning models for real-world applications. You will work on end-to-end ML pipelines , collaborate with cross-functional teams, and apply AI techniques such as NLP, Computer Vision, and Time-Series Forecasting . This role offers opportunities to work on cutting-edge AI solutions while growing your expertise in model deployment and optimization. Key Responsibilities: Design, build, and optimize machine learning models for various business applications. Develop and maintain ML pipelines , including data preprocessing, feature engineering, and model training. Work with TensorFlow, PyTorch, Scikit-learn, and Keras for model development. Deploy ML models in cloud environments (AWS, Azure, GCP) and work with Docker/Kubernetes for containerization. Perform model evaluation, hyperparameter tuning, and performance optimization . Collaborate with data scientists, engineers, and product teams to deliver AI-driven solutions. Stay up to date with the latest advancements in AI/ML and implement best practices. Write clean, scalable, and well-documented code in Python or R.

What will your role look like Design, train, evaluate, and deploy traditional ML models as well as Generative AI-based applications. Work on supervised, unsupervised, and deep learning models including regression, classification, clustering, and sequence models. Build end-to-end ML pipelines including data preprocessing, feature engineering, model training, hyperparameter tuning, and evaluation. Develop and optimize LLM-based workflows using LangChain, LangGraph, and orchestration frameworks. Fine-tune, evaluate, and integrate LLMs such as GPT, LLaMA, Claude, Mistral, Falcon, Cohere, and Gemini. Implement Retrieval-Augmented Generation (RAG) using embeddings and vector stores like FAISS, Pinecone, or Chroma. Apply prompt engineering, LoRA, PEFT, and adapter-based fine-tuning to optimize LLMs for specific tasks. Build Agentic AI systems with tool-use capabilities and reasoning chains (e.g., ReAct, AutoGPT, BabyAGI, CrewAI). Use Hugging Face for leveraging pre-trained models and datasets for rapid experimentation. Collaborate with product, data, and engineering teams to productionize AI solutions using scalable cloud infrastructure. Why you will love this role Besides a competitive package, an open workspace full of smart and pragmatic team members, with ever-growing opportunities for professional and personal growth Be a part of a learning culture where teamwork and collaboration are encouraged, diversity is valued and excellence, compassion, openness and ownership is rewarded. We would like you to bring along Strong grasp of core ML concepts such as model selection, evaluation metrics, bias/variance tradeoff, overfitting/underfitting, etc. Good exposure on using Azure Open AI and hosting applications in Azure environment Experience in building and tuning models using scikit-learn, XGBoost, LightGBM, TensorFlow, or PyTorch. Experience in building ML pipelines with feature engineering, model tuning, cross-validation, and A/B testing. Proficiency in LangChain, LangGraph, and integrating with Hugging Face Transformers Ecosystem. Deep knowledge of various LLMs and techniques like prompt engineering, few-shot learning, and instruction tuning. Experience in building Agentic AI systems and coordinating multi-agent flows or tool-chaining. Familiarity with LLMOps/MLOps tools (e.g., MLflow, Weights & Biases, Kubeflow, SageMaker, or Vertex AI). Strong programming skills in Python, and experience deploying models using FastAPI, Flask, or Streamlit. Experience with cloud platforms (AWS/GCP/Azure) and handling GPU/TPU resources. Solid understanding of data structures, algorithms, and software engineering best Practices. Highly skilled AI/ML Engineer with a solid foundation in Machine Learning and deep hands-on experience in Generative AI (GenAI). Strong capabilities in building, training, and deploying ML models, along with significant experience. working with frameworks such as LangChain, LangGraph, and platforms like Hugging Face, vector databases, and various LLMs. Youll be a key contributor in developing smart assistants, AI agents, and ML solutions that solve complex business problems. Experience with LangSmith, PromptLayer, or other LLM observability tools Familiarity with Guardrails.AI, semantic caching, and output validation techniques Exposure to multi-modal models like CLIP, DALLE, Stable Diffusion, or Whisper Contributions to open-source GenAI or ML libraries/projects Domain expertise in areas like healthcare, finance, manufacturing, or legal tech.

Educational Bachelor of Engineering Service Line Data & Analytics Unit Responsibilities Responsible for successful delivery of MLOps solutions and services in client consulting environments; Define key business problems to be solved; formulate high level solution approaches and identify data to solve those problems, develop, analyze/draw conclusions and present to client. Assist clients with operationalization metrics to track performance of ML Models Agile trained to manage team effort and track through JIRA High Impact Communication- Assesses the target audience need, prepares and practices a logical flow, answers audience questions appropriately and sticks to timeline. Additional Responsibilities: Master’s degree in Computer Science Engineering, with Relevant experience in the field of MLOps / Cloud Domain experience in Capital Markets, Banking, Risk and Compliance etc. Exposure to US/ overseas markets is preferred Azure Certified – DP100, AZ/AI900 Domain / Technical / Tools KnowledgeObject oriented programming, coding standards, architecture & design patterns, Config management, Package Management, Logging, documentation Experience in Test Driven Development and experience in using Pytest frameworks, git version control, Rest APIs Azure ML best practices in environment management, run time configurations (Azure ML & Databricks clusters), alerts. Experience designing and implementing ML Systems & pipelines, MLOps practices Exposure to event driven orchestration, Online Model deployment Contribute towards establishing best practices in MLOps Systems development Proficiency with data analysis tools (e.g., SQL, R & Python) High level understanding of database concepts/reporting & Data Science concepts Hands on experience in working with client IT/Business teams in gathering business requirement and converting into requirement for development team Experience in managing client relationship and developing business cases for opportunities Azure AZ-900 Certification with Azure Architecture understanding is a plus Technical and Professional : Technical knowledge- has expertise in cloud technologies, specifically MS Azure, and services with hands on coding to – Python Programming - Expert and Experienced - 4 -5 years DevOps Working knowledge with implementation experience - 1 or 2 projects a minimum Hands-On MS Azure Cloud knowledge Understand and take requirements on Operationalization of ML Models from Data Scientist Help team with ML Pipelines from creation to execution List Azure services required for deployment, Azure Data bricks and Azure DevOps Setup Assist team to coding standards (flake8 etc) Guide team to debug on issues with pipeline failures Engage with Business / Stakeholders with status update on progress of development and issue fix Automation, Technology and Process Improvement for the deployed projects Setup Standards related to Coding, Pipelines and Documentation Adhere to KPI / SLA for Pipeline Run, Execution Research on new topics, services and enhancements in Cloud Technologies Preferred Skills: Technology-Machine Learning-Python

Project Role : AI / ML Engineer Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing. Must have skills : Machine Learning Operations Good to have skills : NAMinimum 7.5 year(s) of experience is required Educational Qualification : 15 years full time education Summary :As an Machine Learning Engineer/MLOps Expert, you will engage in the operationalization of Machine Learning Models that leverage artificial intelligence tools and cloud AI services. Your typical day will involve designing and implementing production-ready ML system, ensuring high-quality standards are met. Roles & Responsibilities:- Continuously evaluate and improve existing processes to enhance efficiency.- Engage with multiple teams and contribute on key decisions.- Provide solutions to problems for their immediate team and across multiple teams.- Facilitate knowledge sharing sessions to enhance team skills and capabilities.- Monitor project progress and ensure alignment with strategic goals. Professional & Technical Skills: - ML Pipeline Development:Design, build, and maintain scalable pipelines for model training to support our AI initiatives.- Model Deployment & Serving:Deploy machine learning models as robust, secure services containerize models with Docker and serve them via FastAPI on AWS ensuring low-latency predictions for marketing applications. Manage Batch inference and Realtime inference.- CI/CD Automation:Implement continuous integration and delivery (CI/CD) pipelines for ML projects. Automate testing, model validation, and deployment workflows using tools like GitHub Actions to accelerate delivery.- Model Lifecycle Management:Orchestrate the end-to-end ML lifecycle, including versioning, packaging, and registering models. Maintain a model repository/registry (MLflow or similar) for reproducibility and governance from experimentation through production. Experience on MLFlow and Airflow is mandatory- Monitoring & Optimization:Monitor model performance, data drift, and system health in production. Set up alerts and dashboards and proactively initiate model retraining or tuning to sustain accuracy and efficiency over time.- Must To Have Skills: Proficiency in Machine Learning Operations.- Strong understanding of cloud-based AI services and deployment strategies.- Should have Multi Cloud skills- Experience with Machine learning frameworks- Ability to implement and optimize machine learning models for production environments. Additional Information:- The candidate should have minimum 7.5 years of experience in Machine Learning Operations.- This position is based at our Bengaluru office.- A 15 years full time education is required. Qualification 15 years full time education

Project Role : AI / ML Engineer Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing. Must have skills : Machine Learning Operations, Java Good to have skills : NAMinimum 7.5 year(s) of experience is required Educational Qualification : 15 years full time education Summary :As an Machine Learning Engineer/MLOps Expert, you will engage in the operationalization of Machine Learning Models that leverage artificial intelligence tools and cloud AI services. Your typical day will involve designing and implementing production-ready ML system, ensuring high-quality standards are met. Roles & Responsibilities:- Continuously evaluate and improve existing processes to enhance efficiency.- Engage with multiple teams and contribute on key decisions.- Provide solutions to problems for their immediate team and across multiple teams.- Facilitate knowledge sharing sessions to enhance team skills and capabilities.- Monitor project progress and ensure alignment with strategic goals. Professional & Technical Skills: - ML Pipeline Development:Design, build, and maintain scalable pipelines for model training to support our AI initiatives.- Model Deployment & Serving:Deploy machine learning models as robust, secure services containerize models with Docker and serve them via FastAPI on AWS ensuring low-latency predictions for marketing applications. Manage Batch inference and Realtime inference.- CI/CD Automation:Implement continuous integration and delivery (CI/CD) pipelines for ML projects. Automate testing, model validation, and deployment workflows using tools like GitHub Actions to accelerate delivery.- Model Lifecycle Management:Orchestrate the end-to-end ML lifecycle, including versioning, packaging, and registering models. Maintain a model repository/registry (MLflow or similar) for reproducibility and governance from experimentation through production. Experience on MLFlow and Airflow is mandatory- Monitoring & Optimization:Monitor model performance, data drift, and system health in production. Set up alerts and dashboards and proactively initiate model retraining or tuning to sustain accuracy and efficiency over time.- Must To Have Skills: Proficiency in Machine Learning Operations.- Strong understanding of cloud-based AI services and deployment strategies.- Should have Multi Cloud skills- Experience with Machine learning frameworks- Ability to implement and optimize machine learning models for production environments. Additional Information:- The candidate should have minimum 5 years of experience in Machine Learning Operations.- This position is based at our Bengaluru office.- A 15 years full time education is required. Qualification 15 years full time education

Project Role : AI / ML Engineer Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing. Must have skills : Machine Learning Operations, Manual Testing Good to have skills : NAMinimum 5 year(s) of experience is required Educational Qualification : 15 years full time education Summary :As an Machine Learning Engineer/MLOps Expert, you will engage in the operationalization of Machine Learning Models that leverage artificial intelligence tools and cloud AI services. Your typical day will involve designing and implementing production-ready ML system, ensuring high-quality standards are met. Roles & Responsibilities:- Continuously evaluate and improve existing processes to enhance efficiency.- Engage with multiple teams and contribute on key decisions.- Provide solutions to problems for their immediate team and across multiple teams.- Facilitate knowledge sharing sessions to enhance team skills and capabilities.- Monitor project progress and ensure alignment with strategic goals. Professional & Technical Skills: - ML Pipeline Development:Design, build, and maintain scalable pipelines for model training to support our AI initiatives.- Model Deployment & Serving:Deploy machine learning models as robust, secure services containerize models with Docker and serve them via FastAPI on AWS ensuring low-latency predictions for marketing applications. Manage Batch inference and Realtime inference.- CI/CD Automation:Implement continuous integration and delivery (CI/CD) pipelines for ML projects. Automate testing, model validation, and deployment workflows using tools like GitHub Actions to accelerate delivery.- Model Lifecycle Management:Orchestrate the end-to-end ML lifecycle, including versioning, packaging, and registering models. Maintain a model repository/registry (MLflow or similar) for reproducibility and governance from experimentation through production. Experience on MLFlow and Airflow is mandatory- Monitoring & Optimization:Monitor model performance, data drift, and system health in production. Set up alerts and dashboards and proactively initiate model retraining or tuning to sustain accuracy and efficiency over time.- Must To Have Skills: Proficiency in Machine Learning Operations.- Strong understanding of cloud-based AI services and deployment strategies.- Should have Multi Cloud skills- Experience with Machine learning frameworks- Ability to implement and optimize machine learning models for production environments. Additional Information:- The candidate should have minimum 5 years of experience in Machine Learning Operations.- This position is based at our Bengaluru office.- A 15 years full time education is required. Qualification 15 years full time education

About the Role: We are seeking a visionary AI Architect to drive our AI/ML and GenAI roadmap. You will lead the design of multi-modal generative workflows that combine image, video, and LLM-driven tasks for media workflow automation. This role will influence our core platform strategy by integrating custom and open-source diffusion models, prompt-to-image generation, segmentation, and agentic task orchestration using LLMs. Key Responsibilities: Architect AI solutions for image/video generation, segmentation, pose estimation, and retouching using diffusion models and GANs. Design agentic workflows leveraging LLMs (OpenAI, Claude, etc.) for orchestrating creative task chains, model prompting, or hybrid AI-human loops. Lead data pipeline and preparation strategy define data requirements, build annotation pipelines, and guide synthetic data generation. Evaluate and fine-tune foundation models like Stable Diffusion, ControlNet, OpenPose, DeepFaceLab, CP-VTON. Guide research, benchmarking, fine-tuning, and deployment of models. Evaluate build vs. buy decisions for third-party APIs or in-house model development. Ensure compliance with IP, client data protection, fairness, and ethical AI practices. Desired Skills: Deep experience in computer vision, generative AI (diffusion, transformers), and LLM integration Hands-on with PyTorch, Hugging Face, Diffusion/ GAN Architectures Hands-on with LLM and agentic frameworks like LangChain, AWS Bedrock Strong understanding of synthetic data pipelines, model evaluation metrics, and prompt engineering Strong understanding of image harmonization, segmentation, texture transfer Ability to architect scalable ML pipelines combining image/video generation with LLM reasoning Experience leading a technical team or managing AI initiatives end-to-end. Familiarity with AWS/GCP, GPU infrastructure, and scalable AI model serving Familiarity with co-pilot tools and AI-assisted code generation to accelerate prototyping and implementation of AI systems Desired Profile & Qualification: 8+ years in AI/ML roles including leadership in GenAI or CV-heavy platforms Degree in Computer Science, Machine Learning, or related field (MS/PhD preferred). Prior contributions to open-source AI or research publications is an advantage Note: This is an on-site position.

RESPONSIBILITIES: Develop, productionize, and deploy scalable, resilient software solutions for operationalizing AI & ML. Deploy Machine Learning (ML) models and Large Language Models (LLM) securely and efficiently, both in the cloud and on-premises, using state of the art platforms, tools, and techniques. Provide effective model observability, monitoring, and metrics by instrumenting logging, dashboards, alerts, etc. In collaboration with Data Engineers, design and build pipelines for extraction, transformation, and loading of data from a variety of data sources for AI & ML models as well as RAG architectures for LLMs. Enable Data Scientists to work more efficiently by providing tools for experiment tracking and test automation. Ensure scalability of built solutions by developing and running rigorous load tests. Facilitate integration of AI & ML capabilities into user experience by building APIs, UIs, etc. Stay current on new developments in AI & ML frameworks, tools, techniques, and architectures available for solution development, both private and open source. Coach data scientists and data engineers on software development best practices to write scalable, maintainable, well-designed code. Agile Project Work Work in cross-functional agile teams of highly skilled software/machine learning engineers, data scientists, DevOps engineers, designers, product managers, technical delivery teams, and others to continuously innovate AI and MLOps solutions. Act as a positive champion for broader organization to develop stronger understanding of software design patterns that deliver scalable, maintainable, well-designed analytics solutions. Advocate for security and responsibility best practices and tools. Acts as an expert on complex technical topics that require cross-functional consultation. Perform other duties as required. QUALIFICATIONS: Experience applying continuous integration/continuous delivery best practices, including Version Control, Trunk Based Development, Release Management, and Test-Driven Development Experience with popular MLOps tools (e.g., Domino Data Labs, Dataiku, mlflow, AzureML, Sagemaker), and frameworks (e.g.: TensorFlow, Keras, Theano, PyTorch, Caffe, etc.) Experience with LLM platforms (OpenAI, Bedrock, NVAIE) and frameworks (LangChain, LangFuse, vLLM, etc.) Experience in programming languages common to data science such as Python, SQL, etc. Understanding of LLMs, and supporting concepts (tokenization, guardrails, chunking, Retrieval Augmented Generation, etc.). Knowledge of ML lifecycle (wrangling data, model selection, model training, modeling validation and deployment at scale) and experience working with data scientists Familiar with at least one major cloud provider (Azure, AWS, GCP), including resource provisioning, connectivity, security, autoscaling, IaC. Familiar with cloud data warehousing solutions such as Snowflake, Fabric, etc. Experience with Agile and DevOps software development principles/methodologies and working on teams focused on delivering business value. Experience influencing and building mindshare convincingly with any audience. Confident and experienced in public speaking. Ability to communicate complex ideas in a concise way. Fluent with popular diagraming and presentation software. Demonstrated experience in teaching and/or mentoring professionals.

We are looking for an experienced Machine Learning Engineer to lead the development and deployment of ML models that power intelligent products and insights. You will collaborate with teams across Data Science, Engineering, and Product to build solutions that are scalable, efficient, and impactful. Candidates with exposure to the healthcare domain are encouraged to apply, although this is not a mandatory requirement . Key Responsibilities: Architect, build, and maintain end-to-end ML systems from data pipelines to model deployment. Develop and optimize machine learning models for use cases such as classification, prediction, recommendation, NLP, or computer vision. Implement MLOps best practices for model training, tracking, deployment, and monitoring. Collaborate with data scientists and domain experts to productionize prototypes and research. Evaluate and monitor model performance; ensure robustness, fairness, and explainability. Document architecture and processes; contribute to knowledge sharing and code reviews. (Preferred) Work with EHR data, claims data, clinical notes, or healthcare interoperability formats like HL7 or FHIR, if applicable. Required Skills & Qualifications: Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or related discipline. 5+ years of hands-on experience in ML engineering or applied data science. Strong command of Python and ML libraries (e.g., scikit-learn, TensorFlow, PyTorch, XGBoost). Experience deploying ML models in production using containerization (Docker, Kubernetes) and cloud platforms (AWS/GCP/Azure). Familiarity with MLOps tools like MLflow, DVC, or Kubeflow. Proficient in building ETL/ELT pipelines and handling large-scale datasets. Strong understanding of statistical methods, model evaluation metrics, and optimization techniques. Good software engineering practices (version control, testing, CI/CD). Preferred Qualifications: Exposure to healthcare datasets such as medical claims, EHR/EMR, HL7, FHIR, or medical coding (CPT, ICD-10). Experience with NLP models applied to clinical documentation or unstructured medical data. Understanding of HIPAA compliance, data anonymization, and PHI handling. Contributions to open-source ML projects or peer-reviewed publications. Experience working in regulated industries or mission-critical environments.

We're hiring experienced professionals in our growing Data science Practice at EY GDS Hyderabad Office . We are looking for talented professionals to join our team. Below are the details for the open positions: Position : AI and DATA -ML Ops Engineers Experience : 4-12 years Location : Hyderabad Programming Languages: Proficiency in Python (3.x) and SQL. ML Frameworks & Libraries: Extensive knowledge of ML frameworks (TensorFlow, PyTorch, Scikit-learn), data structures, data modelling, and software architecture. Databases: Experience with SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Cassandra, DynamoDB) databases. Mathematics & Algorithms: Strong understanding of mathematics, statistics, and algorithms for machine learning applications. ML Modules & REST API: Experience in developing and integrating ML modules with RESTful APIs. Version Control: Hands-on experience with Git and best practices for version control. Model Deployment & Monitoring: Experience in deploying and monitoring ML models using: MLflow (for model tracking, versioning, and deployment) WhyLabs (for model monitoring and data drift detection) Kubeflow (for orchestrating ML workflows) Airflow (for managing ML pipelines) Docker & Kubernetes (for containerization and orchestration) Prometheus & Grafana (for logging and real-time monitoring) Data Processing: Ability to process and transform unstructured data into meaningful insights (e.g., auto-tagging images, text-to-speech conversions) Apply Here: https://careers.ey.com/job-invite/1606302/ Please share the updated resume to Gayathri.s8@gds.ey.com . We look forward to discussing your potential with EY!

Dear Candidate, We are seeking a Machine Learning Engineer to develop predictive models and deploy them into production. Ideal for professionals passionate about AI and data science. Key Responsibilities: Develop and train machine learning models Preprocess and analyze large datasets Deploy models using scalable infrastructure Collaborate with product teams to integrate ML solutions Required Skills & Qualifications: Strong knowledge of Python and ML libraries (scikit-learn, TensorFlow, PyTorch) Experience with data preprocessing and feature engineering Familiarity with model deployment techniques Bonus: Experience with cloud ML services (AWS SageMaker, Google AI Platform) Soft Skills: Strong troubleshooting and problem-solving skills. Ability to work independently and in a team. Excellent communication and documentation skills. Note: If interested, please share your updated resume and preferred time for a discussion. If shortlisted, our HR team will contact you. Kandi Srinivasa Delivery Manager Integra Technologies

Design, develop, and deploy AI/ML models; build scalable, low-latency ML infrastructure; run experiments; optimize algorithms; collaborate with data scientists, engineers, and architects; integrate models into production to drive business value. Required Candidate profile 5–10 yrs in AI/ML, strong in model development, optimization, and deployment. Skilled in Azure, ML pipelines, data science tools, and collaboration with cross-functional teams.

This is an urgent and fast filling position - Need immediate joiners OR l1 month notice period We are Looking for 1)Junior AI/ML Engineer - Positions 2 open 2)Mid-level AI/ML Engineer -1 position open 3)Lead AI/ML Engineer - 1 position open Location: Ambattur, Chennai Fulltime position Job Summary: We are looking for a AI/ML Engineer to develop, optimize, and deploy machine learning models for real-world applications. You will work on end-to-end ML pipelines , collaborate with cross-functional teams, and apply AI techniques such as NLP, Computer Vision, and Time-Series Forecasting . This role offers opportunities to work on cutting-edge AI solutions while growing your expertise in model deployment and optimization. Key Responsibilities: Design, build, and optimize machine learning models for various business applications. Develop and maintain ML pipelines , including data preprocessing, feature engineering, and model training. Work with TensorFlow, PyTorch, Scikit-learn, and Keras for model development. Deploy ML models in cloud environments (AWS, Azure, GCP) and work with Docker/Kubernetes for containerization. Perform model evaluation, hyperparameter tuning, and performance optimization . Collaborate with data scientists, engineers, and product teams to deliver AI-driven solutions. Stay up to date with the latest advancements in AI/ML and implement best practices. Write clean, scalable, and well-documented code in Python or R. Technical Skills: Programming Languages: Proficiency in languages like Python. Python is particularly popular for developing ML models and AI algorithms due to its simplicity and extensive libraries like NumPy, Pandas, and Scikit-learn. Machine Learning Algorithms: Should have a deep understanding of supervised learning (linear regression, decision trees, SVM), unsupervised learning, and reinforcement learning. Data Management and Analysis: Skills in data cleaning, feature engineering, and data transformation are crucial. Deep Learning: Familiarity with neural networks, CNNs, RNNs, and other architectures is important. Machine Learning Frameworks and Libraries: Experience with TensorFlow, PyTorch, Keras, or Scikit-learn is valuable. Natural Language Processing (NLP): Familiarity with NLP techniques like word2vec, sentiment analysis, and summarization can be beneficial. Cloud Computing: Experience with cloud-based services like AWS SageMaker, Google Cloud AI Platform, or Microsoft Azure Machine Learning. Data Preprocessing: Skills in handling missing data, data normalization, feature scaling, and data transformation. Feature Engineering: Ability to create new features from existing data to improve model performance. Data Visualization: Familiarity with visualization tools like Matplotlib, Seaborn, Plotly, or Tableau. Containerization: Knowledge of containerization tools like Docker and Kubernetes. Databases : Understanding of relational databases (e.g., MySQL) and NoSQL databases (e.g., MongoDB). Data Warehousing: Familiarity with data warehousing concepts and tools like Amazon Redshift or Google BigQuery. Computer Vision: Understanding of computer vision concepts and techniques like object detection, segmentation, and image classification. Reinforcement Learning: Knowledge of reinforcement learning concepts and techniques like Q-learning and policy gradients.

Role Summary: We are seeking an experienced and talented Senior Engineer to join our MLOps team. In this role, you will play a crucial part in designing, developing, and maintaining scalable and reliable machine learning operations (MLOps) pipelines and infrastructure. You will collaborate closely with data scientists, software engineers, and other stakeholders to ensure the successful deployment and monitoring of machine learning models in production environments. Responsibilities: Design and implement robust MLOps pipelines for model training, evaluation, deployment, and monitoring using industry-standard tools and frameworks Collaborate with data scientists to streamline the model development process and ensure seamless integration with MLOps pipelines. Optimize and scale machine learning infrastructure to support high-performance model training and inference. Contribute to the development of MLOps standards, processes, and documentation within the organization. Mentor and support junior team members in MLOps practices and technologies. Stay up-to-date with the latest trends and best practices in MLOps, and explore opportunities for continuous improvement. Qualifications: Bachelor's or Master's degree in Computer Science, Statistics, or a related field. 5+ years of experience in software engineering, with 2+ years experience in ML Proficient in Python and at least one other programming language (e.g., Java, Go, C++). Extensive experience with containerization technologies (Docker, Kubernetes) and cloud platforms (AWS, GCP, Azure). Familiarity with machine learning frameworks and MLOps tools Experience with big data technologies Strong understanding of CI/CD principles and practices. Preferred Qualifications: Familiarity with model serving frameworks Knowledge of infrastructure as code (IaC) tools Experience with monitoring and observability tools Contributions to open-source MLOps projects or communities.