Top Machine Learning Jobs in India

Research, prototype, evaluate, and productionize AI/ML capabilities for engineering and industrial use cases. Build agent-accessible artifacts, reusable software components, evaluation and benchmarking practices, and production-ready platform integrations. Collaborate with data engineers, software engineers, cloud architects, and engineering stakeholders while using GCP, GitHub workflows, automated testing, and CI/CD. Communicate findings through documentation, publications, patents, or open-source contributions.

Program-manage multiple machine learning initiatives from scoping through production, coordinating Product, Engineering, Clinical, and Customer teams. Establish roadmaps, milestones, risk management, evaluation frameworks, and operating rhythms for LLM, RAG, clinical NLP, and MLOps programs. Review technical designs and experiments, promote model reliability and observability, and mentor ML engineers and data scientists while improving processes and delivery scale.

Lead the architecture, development, deployment, and operation of enterprise machine learning and generative AI solutions. Build anomaly detection, fraud detection, recommendation, forecasting, and predictive systems, along with LLM, RAG, vector search, and agentic applications. Own MLOps, data pipelines, model monitoring, retraining, optimization, and evaluation across thousands of tenants. Partner cross-functionally, mentor engineers, conduct reviews, and drive AI innovation from prototyping through production.

Develop, validate, debug, and optimize machine learning models, training pipelines, inference systems, and numerical implementations using Python. Build reproducible workflows, diagnose computational issues, create systematic tests, and improve memory usage, throughput, and performance. Review code and solutions, use coding agents in development, document technical findings, and deliver reliable work independently within project deadlines.

Design, build, and scale production AI agent workflows using AWS Bedrock AgentCore, Step Functions, Claude, MCP, RAG, and guardrails. Develop Python/FastAPI backends, agent-facing interfaces, document-processing pipelines, enterprise integrations, and AWS infrastructure. Implement observability, evaluations, security controls, human approval workflows, and CI/CD. Work directly with enterprise customers and nontechnical stakeholders to deliver end-to-end AI solutions with minimal guidance.

Build and deploy production LLM and machine learning solutions, including RAG pipelines, agentic workflows, document intelligence, classical ML models, evaluation harnesses, guardrails, APIs, and cloud deployments. The role emphasizes measurable quality, cost control, retrieval optimization, MLOps, model selection, and client-facing technical solutioning. Candidates need strong Python, LLM application development, vector database, ML, evaluation, API, Docker, cloud, and Git experience, with 4–7 years in ML or software engineering and at least two years building generative AI applications.

Leads the architecture and production implementation of enterprise AI and machine learning solutions. Responsibilities include cloud-native infrastructure, AI security and governance, model monitoring, optimization, lifecycle management, Python API development, and cross-functional technical leadership. The role requires extensive experience designing scalable AI/ML architectures and deploying solutions across AWS, Azure, or Google Cloud.

Designs, develops, deploys, and maintains production-grade AI/ML solutions using deep learning, neural networks, AWS, and Amazon SageMaker. Responsibilities include building ML pipelines, developing chatbots and conversational AI, applying computer vision to image data, monitoring deployed models, and collaborating with teams to translate business needs into AI strategies. Requires 8–12 years of experience and expert-level AWS and machine learning expertise.

Develop and deploy scalable AI and machine learning solutions using Generative AI, LLMs, RAG, NLP, and Computer Vision. Manage model lifecycles with Docker, Kubernetes, and MLOps practices. Optimize vector databases, prompt engineering, and model performance while conducting SQL-based data analysis and statistical modeling. Integrate AI capabilities into business workflows and maintain reliable, scalable deployment pipelines.

Test data pipelines, AI/ML models, and LLM workflows for data quality, functionality, performance, scalability, and accuracy. Develop SQL and Python test cases, validate transformations and storage, use Databricks, PySpark, Pandas, and Apache Spark to assess large-scale data systems, identify anomalies, troubleshoot issues with development teams, and monitor data quality throughout the software lifecycle.

Designs and deploys scalable machine learning solutions using Python, TensorFlow, Kubeflow, Docker, and Kubernetes. Develops LLM applications and RAG architectures, integrates OpenAI and other AI services into production, manages cloud-native orchestration workflows, and monitors model and infrastructure performance to ensure reliability and continuous improvement.

Lead design and implementation of ML tools for scenario classification, anomaly detection, sensor data quality, behavior analytics, and automated reporting. Build scalable pipelines and automated data curation for camera, radar, and LiDAR; create SIL/HIL replay interfaces, ground-truthing and annotation tools, and offline benchmarking. Integrate ML modules into simulation, HIL/SIL, and CI/CD ecosystems, implement reproducible workflows (Docker, CI/CD, artifact versioning, automated tests), monitor model/data quality and mentor junior engineers while collaborating with verification and perception teams.

Leads data science initiatives by translating business problems into projects, preparing and analyzing data, engineering features, developing and validating machine learning models, and monitoring performance. Uses Python, SQL, Databricks, and big data platforms. Communicates insights to technical and non-technical stakeholders, documents processes, promotes best practices, and mentors junior data scientists while supporting team development and collaboration.

Designs, builds, tests, and deploys advanced machine learning and NLP systems for complex business challenges. Researches zero-shot and few-shot learning, embeddings, fine-tuning, transformers, and LLM applications. Develops scalable, secure, high-throughput architectures and reusable modeling tools using Python and modern ML infrastructure. Collaborates with data scientists, manages stakeholder communications, handles shifting priorities, and promotes high-quality production code and knowledge sharing.

Design, operate, and evolve ResMed’s AI/ML platform on AWS and Kubernetes. Provision infrastructure with Terraform, own CI/CD for models and applications, and build observability with Prometheus, Loki, Grafana, or Datadog. Partner with data science and GenAI teams on model serving, agentic workloads, autoscaling, secure tool integration, and cost and latency controls. The role also automates platform workflows, supports LLMOps and RAG infrastructure, runs proofs of concept, and contributes to code reviews and mentoring.

Build backend and platform components for an AI agent layer, including guardrails, policy enforcement, semantic caching, MCP-based tool governance, access controls, and application integrations. Develop scalable APIs and services, integrate across four applications, write comprehensive tests, troubleshoot production and performance issues, and collaborate with architecture and application teams on secure, reliable integrations.

Leads the design and development of generative and agentic AI applications using LLMs, NLP, deep learning, retrieval, RAG, and orchestration frameworks. Builds agent architectures, evaluation frameworks, data-processing pipelines, guardrails, and responsible AI controls. Optimizes models through prompting, reranking, embeddings, model selection, and fine-tuning. Translates business needs into measurable AI solutions, stays current with emerging technologies, leads technical discussions, and mentors team members.

Design and deliver production-grade AI/ML architectures for education and assessment platforms. Translate stakeholder requirements into secure, cost-aware blueprints covering RAG, multi-agent systems, LLM judging, OCR, human review, cloud infrastructure, LLMOps/MLOps, observability, governance, and evaluation. Lead workshops, architecture decisions, production readiness, FinOps, sovereign hosting, and technical mentorship across international government and education programs.

Build and deploy enterprise AI/ML and Generative AI applications across the full lifecycle, including data pipelines, RAG systems, agentic workflows, APIs, evaluation, monitoring, and cloud deployment. Develop production Python services, CI/CD workflows, containerized applications, and secure AI solutions. Collaborate with product, engineering, cloud, data, and business teams while documenting architectures and participating in agile delivery.

Leads the design, development, deployment, and operation of production AI, GenAI, RAG, and agentic solutions. Responsibilities include building data and model pipelines, MCP integrations, evaluation and observability systems, secure cloud-native services, and enterprise connectors. The role requires hands-on Python engineering, AWS or Azure deployment, MLOps/LLMOps practices, architecture leadership, stakeholder collaboration, and mentoring. Production MCP experience and a current AWS AI/ML or Azure AI certification are mandatory.

Lead the design, development, deployment, and operation of machine learning, generative AI, RAG, and agentic AI solutions. Responsibilities include model optimization, AI architecture, MLOps and GenAIOps, monitoring, governance, security, responsible AI, production troubleshooting, and cross-functional delivery of scalable enterprise AI services.

Build and productionize agentic AI and LLM features for an enterprise SaaS platform focused on data privacy and AI governance. Responsibilities include developing LangChain and LangGraph workflows, RAG pipelines, AI asset discovery, safety guardrails, model evaluations, backend API integrations, and observability. The role requires Python, cloud AI platforms across AWS and Azure, vector databases, prompt engineering, and experience deploying ML or GenAI applications in production.

Build and maintain production-grade machine learning pipelines and APIs for industrial data contextualization. Responsibilities include deploying research prototypes, writing testable Python, containerizing applications with Docker, developing RESTful and gRPC services, fine-tuning NLP and vision-language models, processing large datasets, monitoring model performance, and collaborating with engineering, data science, and product teams.

Designs, develops, and optimizes enterprise data platforms, ETL/ELT pipelines, data warehouses, integration frameworks, and analytics solutions. Leads architecture, technical design, performance tuning, DevOps automation, troubleshooting, and production readiness. Partners with product, architecture, analytics, AI/ML, and business teams to deliver governed, scalable data foundations. Mentors engineers, conducts technical and code reviews, and supports modernization using Talend, Snowflake, Databricks, Python, SQL, and cloud technologies.

Design, develop, and deploy ML models on Google Cloud (Vertex AI). Process and feature-engineer large datasets in BigQuery, integrate ML pipelines with GCP services, collaborate with stakeholders, and produce analytical reports and visualizations to support data-driven decisions.