Top Machine Learning Jobs in India

Research and develop domain-specific LLMs and generative models for materials science: pretraining, fine-tuning, alignment (e.g., RLHF), evaluation, dataset curation, and benchmark creation. Collaborate with scientists and engineers to apply generative AI to discovery and hardware design, publish research, and mentor junior team members.

Hands-on techno-functional specialist to design, build, and integrate AI/LLM-driven features into enterprise project management systems (Jira, Asana, Smartsheet). Responsibilities include prompt engineering, agent automation, ML integration, SDLC governance, tool integration, stakeholder adoption, and leading end-to-end delivery of AI-enabled PPM/portfolio capabilities.

Develop automation scripts, internal support tools, data pipelines, and AI/ML solutions for Technical Support. Collaborate with Support, Quality, and Engineering teams to troubleshoot medical communication products, analyze customer logs, eliminate manual processes, and deliver scalable enterprise solutions. Responsibilities include designing, building, documenting, testing, and supporting tools while following software engineering, Agile, version-control, and code-review best practices.

Develop automation scripts, internal support tools, data pipelines, and AI/ML solutions for Technical Support. Collaborate with Support, Quality, and Engineering teams to troubleshoot products, analyze customer log files, eliminate manual processes, and deliver scalable enterprise solutions. Responsibilities include designing features, supporting existing tools, following software engineering practices, documenting capabilities, and working within Agile development processes.

Design and implement scalable end-to-end AI/ML solutions and data pipelines; define secure cloud and on-prem architectures; lead MLOps, CI/CD, model lifecycle management, data governance, and cross-functional collaboration to drive AI initiatives in manufacturing.

Develop high-level machine learning architectures, build and productionize models and end-to-end ML systems, and deliver data-driven insights for clients. The role involves Python, PySpark, SQL, NLP, LLM, GenAI, statistical modeling, cloud services, and data pipeline development. Responsibilities also include client relationship management, mentoring junior engineers, coordinating delivery teams, identifying sales opportunities, and communicating business impact.

The Software Engineering Lead Analyst will design and implement AI-driven solutions using Python, machine learning, and modern AI tools while enhancing development processes and tools.

Develop LLM-powered intelligent agents, autonomous workflows, multimodal AI systems, RAG pipelines, and prompt strategies for embedded and edge automotive platforms. Optimize real-time inference using TensorFlow Lite, quantization, and NPU/GPU acceleration. Build microservices and distributed AI architectures, integrate agents into software development workflows, and automate activities such as defect analysis, requirements processing, debugging, and testing.

Design, productionize, optimize, and deploy large-scale AI and machine learning models and infrastructure. Build distributed training and inference systems, resilient data pipelines, evaluation suites, monitoring, logging, and ML CI/CD workflows. Collaborate with research, data, and platform engineers to turn prototypes into scalable, low-latency applications. Provide technical leadership through code reviews, mentoring, and engineering best practices.

Lead input validation and production data operations for NielsenIQ's retail data platforms (CIP/GRFE/SIRVAL/GSRS). Execute retailer/vendor data acquisition, validation, RCA, automation (R/Python/Excel Macros), stakeholder communication, KPI tracking, and process improvement to ensure timely, accurate data delivery and support cross-functional teams.

Build, train, and deploy production machine learning models and maintain end-to-end ML pipelines. Manage AWS infrastructure using VPC, EC2, EKS, EMR, S3, and RDS; contribute to architecture decisions; write production-quality code; collaborate across teams; and mentor junior engineers. The role requires expertise in Python, SQL, Spark, modern AI techniques including LLMs or Generative AI, and machine learning methods for large datasets.

Lead an engineering pod building and operating Verizon’s enterprise AI governance control plane. Architect automated governance pipelines, integrate AI development platforms with security and compliance hubs, implement agent simulation and real-time LLM observability, build governance APIs, and design IAM and routing for secure autonomous workflows. The role also advances enterprise AI standards, FinOps practices, evaluation metrics, and responsible AI innovation.

Perform advanced analytics on large datasets, design and build automated data pipelines, investigate data quality and lineage, and create interactive dashboards. Translate insights into actionable campaign and process recommendations, partner with stakeholders and support campaign delivery and BAU operations.

Perform data investigation, design ETL/data pipelines, apply advanced analytics and ML to large datasets, create dashboards and automated reports, and translate insights into actionable campaign and operational recommendations while managing stakeholders and team responsibilities.

Develop and support secure IoT software applications, APIs, and UIs; build dashboards, ETL/data workflows, and analytics; enable ML/AI capabilities; maintain and modernize legacy code hosted on AWS.

Design, build and maintain scalable automation frameworks and test suites for cloud/big-data systems. Implement automated and manual tests across UI, services, APIs and performance; deploy via CI/CD; collaborate with cross-functional teams and provide technical guidance.

Lead and mentor a strategy technology team to design, implement, test and deploy quantitative trading systems (including C++ auto-traders) for Indian and APAC exchanges. Partner with traders and researchers, own technical architecture and delivery, drive engineering best practices, monitoring, and cross‑office collaboration to expand Optiver's systematic trading capability.

Lead design and implementation of LLM/Generative AI solutions, integrating models into production via APIs and microservices. Develop, evaluate, and benchmark NLP and ML systems; build scalable full-stack applications using NodeJS, React, Postgres, AWS, Docker/Kubernetes. Ensure model trustworthiness, monitor performance, and apply prompt engineering, RAG, and fine-tuning techniques.

Lead global weather-to-power methodology for wind and solar production forecasting: design and validate resource assessment frameworks, integrate satellite/reanalysis/NWP data, apply ML/AI methods, calibrate against SCADA and grid data, mentor analysts, ensure quality control, and present findings to clients and stakeholders.

Lead end-to-end ML systems: data pipelines, model development (transformer-based detection), sim-to-real synthetic data, multi-sensor fusion, on-device real-time inference (Jetson), MLOps (experiment tracking, versioning), and production deployment and monitoring. Set ML roadmap and hire/lead as the team grows.

Develop, test, and deploy machine learning and generative AI solutions (including LLM-based chatbots). Perform data preprocessing, feature engineering, EDA, model fine-tuning and evaluation, and collaborate to integrate models into production while documenting experiments.

Maintain and enhance a Kedro-based lead generation engine: update pipelines, troubleshoot production issues, write Python workflows and SQL, support Databricks and Power BI, improve reliability and scalability, and contribute to machine learning and data science initiatives while collaborating with stakeholders.

Lead design and implementation of a modern verification and CI/CD platform, modernize pipelines with AI/ML-driven risk prediction, integrate automated security and compliance, own release automation, mentor engineers, collaborate cross-team, write design docs, and participate in on-call incident response.

Design and maintain enterprise IT and data architecture focused on data governance, MDM, metadata/catalog, and data quality. Lead technical design, implement MDM and metadata solutions, enable AI-driven automation, collaborate with stakeholders, mentor architects, and recommend technologies to improve data discoverability, reliability, and business value.

Design, develop, and execute end-to-end tests (including automated solutions using Python and Behave) for Order Entry and Market Data systems. Lead release validation, analyze test results, track defects, maintain test environments, and collaborate with developers, QA, and support teams to ensure high-quality production releases.