Top Machine Learning Jobs in India

Designs, develops, and maintains scalable full-stack applications using React.js and Java/Spring Boot. Builds REST APIs, microservices, event-driven Kafka architectures, and AI-powered capabilities. Deploys and troubleshoots cloud-native applications in Kubernetes environments, contributes to technical architecture, and ensures performance, security, reliability, and scalability. Collaborates cross-functionally, participates in code reviews, mentors junior engineers, and uses AI-assisted development tools to improve productivity and software quality.

Develop and deploy production-ready Generative AI, RAG, NLP, and machine learning solutions. Advise teams on appropriate AI approaches, customize and optimize RAG pipelines, fine-tune transformer and large language models, evaluate model performance, implement responsible AI guardrails, and manage end-to-end data science project lifecycles. Collaborate with subject matter experts and technology teams to operationalize data science pipelines using cloud platforms and efficient Python code.

Develop and deploy production-ready Generative AI, RAG, NLP, and machine learning solutions. Partner with Clinical Solutions teams to assess technical approaches, customize RAG pipelines, optimize content ingestion and contextual retrieval, fine-tune transformer and language models, evaluate performance, and implement responsible AI guardrails. Build Python-based data science pipelines across the full lifecycle, collaborate with subject matter experts and technology teams, and operationalize solutions using cloud platforms and AI agents.

Build and optimize scalable data engineering pipelines, feature stores, data models, and workflows for structured, semi-structured, and unstructured data. Integrate diverse data sources using AWS and Snowflake, implement transformations and quality checks, and support data governance, security, and management. The role also involves web crawling, API and stream-based extraction, workflow automation, root-cause analysis, and collaboration with cross-functional stakeholders in a hybrid HPE office environment.

Architect and deploy advanced machine learning and agentic AI solutions, designing scalable workflows from data ingestion through inference. Build cloud-native microservices and APIs, integrate models with data lakes, Kubernetes, and event-driven platforms, and ensure governance, monitoring, performance, and reliability. Collaborate with data engineering, evaluate emerging frameworks and cloud services, conduct benchmarking and A/B testing, document systems, and mentor junior engineers.

Develop and adapt small language models for specialized domain applications. Responsibilities include selecting base models, implementing efficient fine-tuning, curating and synthesizing datasets, designing executable evaluations, applying distillation and quantization, deploying models across GPU, CPU, and edge environments, and maintaining reproducible research and training pipelines. The role also translates recent machine learning research into practical experiments and production-ready solutions.

Develop and deploy production-grade AI systems and agentic workflows in enterprise environments. Embed with client teams to design architectures, integrate AI platforms with ERP, CRM, ITSM, and data systems, and own deployments from experimentation through production handover. Lead workshops and architecture sessions, quantify business impact for senior stakeholders, and create reusable implementation patterns. Responsibilities include cloud-native engineering, multi-provider AI platform integration, governance, monitoring, CI/CD, infrastructure as code, and end-to-end delivery ownership.

Develop and optimize machine learning models, perform data mining and statistical analysis, integrate and cleanse data, and build forecasting validation scripts. Address customer concerns involving forecasting and data quality while collaborating with engineering and product teams. The role requires Python, SQL, NLP, LLM, neural networks, supply chain knowledge, operations research, data visualization, and applied statistics, with occasional customer escalations and flexible-hours support.

Build and scale enterprise agentic AI platforms, custom connectors, secure APIs, and complex multi-agent workflows. Integrate enterprise applications and data sources, optimize platform performance and reliability, establish monitoring and observability, troubleshoot advanced issues, and maintain security, compliance, documentation, and lifecycle processes. The role also supports GenAI and traditional ML pipelines, MLOps practices, cloud AI services, and technical mentorship.

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.

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.

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.