Top Machine Learning Jobs in India

Reviews trade surveillance alerts across equities, derivatives, fixed income, and commodities to identify regulatory or policy breaches. Investigates and documents escalated alerts, supports control design and optimization, prepares management information, develops surveillance metrics, and assists with regulatory inquiries, audits, testing, and technology enhancements. Collaborates with Compliance, Operations, Technology, Risk, vendors, and front-office teams across regions.

Build, fine-tune, validate, deploy, monitor, and debug AI models for high-volume manufacturing operations. Responsibilities include defining defect-labeling rules, curating image datasets, analyzing model errors, conducting root-cause analysis, reporting performance, and coordinating with customers and internal teams. The role requires reviewing large image volumes, maintaining production model accuracy, and supporting projects through completion, with occasional customer-site travel.

Build production AI systems for finance teams, including per-customer anomaly detection models, grounded natural-language generation, agentic workflows, and managed LLM integrations. Develop Python services with SQL and warehouse access in containerized customer deployments. Establish rigorous evaluation, anti-leakage, hallucination, grounding, and faithfulness controls, incorporate human confirmation checkpoints, and convert user feedback into durable model and system improvements.

Own core backend services for a finance AI platform, building reliable, configuration-driven computation systems over financial and operational data. Ensure results are traceable, reproducible, auditable, and correctly reconciled. Provide technical leadership through architecture, design reviews, engineering standards, and mentorship while partnering with data, AI, and product teams. The role involves production Python, advanced SQL and data modeling, multi-tenant or customer-hosted deployments, and high standards for correctness and system reliability.

Lead product vision and discovery for a genomics-driven diagnostic and personalization platform. Validate hypotheses with patients, clinicians, and partners; translate research into product specs; coordinate labs and data providers; partner with engineers and researchers to build AI prototypes; shape data, regulatory, and GTM strategy while making core product and architectural decisions.

Build core components of a diagnostic and personalization platform: backend data pipelines, frontends, services for genomic data ingestion, ML model integration, APIs, and cloud infrastructure. Collaborate with product and ML teams, iterate on prototypes and MVPs, and make architectural decisions supporting scalability and reliability.

Leads AI and machine learning development initiatives, manages engineering teams, designs algorithms and models, mentors engineers, and oversees deployment of AI solutions.

Design and architect high-scale, geo-distributed web platforms supporting Microsoft Office applications. Lead performance benchmarking, scalability, reliability, modernization, and secure software development. Build and test features, develop proofs of concept, define components, and collaborate across engineering teams. Identify AI productivity opportunities and promote engineering excellence through technical leadership, communication, and influence.

Build scalable data science and optimization solutions for supply chain and logistics, including network design and first/last-mile optimization. Analyze large datasets, develop mathematical formulations and heuristics, apply machine learning and advanced optimization techniques, and translate model results into actionable business insights. Collaborate with Product, Engineering, and Business teams while owning problems from discovery through scalable implementation.

Leads AI Engineering and Data Science strategy, architecture, reusable capabilities, and enterprise delivery across Lonza. Drives Generative AI, Agentic AI, machine learning, MLOps, governance, evaluation, deployment, monitoring, and responsible AI practices. Provides technical leadership for production-scale AI solutions, builds global teams, develops engineering standards, and partners with stakeholders to industrialize AI products in regulated environments.

Leads and mentors software engineering teams developing AI and computer vision solutions. Provides technical direction across architecture, development, testing, deployment, CI/CD, troubleshooting, and product delivery. Manages planning, priorities, risks, dependencies, stakeholder collaboration, onboarding, performance development, and continuous improvement. Maintains hands-on expertise in Python and C/C++ within Linux environments while promoting engineering standards, knowledge sharing, innovation, and organizational resilience.

Write technically accurate case studies, articles, product explainers, launch materials, and social content for AI engineers, researchers, and technical leaders. Interview customers, partners, and internal experts; learn HUD’s platform and workflows; explain technical products and research clearly; support founder communications; manage content from ideation through publication; and improve content using reader and go-to-market feedback.

Develop, train, evaluate, and deploy small task-specific ML models for real-time English conversational skills (ASR, pronunciation, grammar, recommendations). Create datasets and task-specific evaluations, improve internal SOTA, mentor engineers, and contribute to hiring.

Develop generative AI applications, chatbots, intelligent agents, RAG pipelines, semantic search systems, and enterprise automation solutions. Build Python backend services and REST APIs, integrate commercial and open-source language models, use AI development frameworks, support AWS deployment, and perform prompt engineering, testing, debugging, and optimization. Collaborate with engineering and product teams to deliver maintainable, production-ready AI software.

Develop and deploy generative AI and machine learning models using Python and cloud platforms such as Azure or AWS. Build data pipelines, preprocessing utilities, and feature engineering systems; integrate and optimize models; apply prompt engineering and agent frameworks; collaborate cross-functionally; and mentor junior team members.

Design, develop, deploy, and integrate AI, machine learning, and generative AI solutions for industrial and business applications. Own the machine learning lifecycle, define architectures, assess data readiness, monitor production models, and ensure reliability and maintainability. Evaluate emerging AI technologies, manage technical delivery from partners and contractors, and collaborate with cross-functional stakeholders to identify use cases, support adoption, and deliver measurable operational improvements.

Designs, develops, deploys, and integrates AI, machine learning, deep learning, and generative AI solutions for industrial and business applications. Owns the machine learning lifecycle, solution architecture, data strategy, model monitoring, and production integration. Evaluates emerging technologies, manages technical delivery from partners and contractors, and collaborates with cross-functional teams to deliver scalable, reliable, and maintainable AI solutions.

Designs, develops, deploys, and integrates AI, machine learning, deep learning, and generative AI solutions for industrial and business challenges. Responsibilities include selecting modeling approaches, managing the ML lifecycle, defining solution architectures, analyzing operational data, supporting production deployment and monitoring, managing technical partners, and ensuring data governance, reliability, and maintainability. The role also evaluates emerging technologies, collaborates across functions, and supports user adoption and continuous innovation.

Designs, develops, deploys, and integrates AI, machine learning, generative AI, and analytics solutions for industrial and business challenges. Owns the machine learning lifecycle, solution architecture, data strategy, production integration, monitoring, and technical evaluation of internal and external partners. Collaborates with cross-functional stakeholders, supports adoption, evaluates emerging AI technologies, and develops solutions such as predictive maintenance, anomaly detection, forecasting, document analysis, and knowledge tools.

Develop and improve perception systems for deployed Stretch robots, including object detection, localization, state estimation, calibration, sensor infrastructure, and data pipelines. Write maintainable C++ and Python code, test robots in varied warehouse conditions, analyze performance, debug real hardware and data, and collaborate with controls, manipulation, and platform teams.

Supports marketing budgeting, strategic planning, forecasting, and performance reporting across channels, countries, and brands. Analyzes campaign results, identifies optimization and growth opportunities, recommends spend reallocations, and develops actionable insights for leadership. Partners with Analytics, Data Science, Finance, and Marketing on forecasting, bidding models, experimentation, dashboards, and marketing P&L tracking. Uses AI tools, SQL, and Python to automate reporting and improve insight generation.

Leads an integrated data, AI, analytics, and product engineering organization. Owns strategy, roadmaps, architecture, technical standards, production service levels, AI governance, client relationships, budgets, hiring, and organizational growth. Directs delivery of data platforms, dashboards, machine learning models, generative AI solutions, and full-stack applications while ensuring measurable business value, operational stability, responsible AI practices, and strong stakeholder governance.

Builds and maintains scalable data pipelines, data architecture, ETL infrastructure, and analytics tools. The role assembles complex datasets, improves data delivery and infrastructure scalability, supports cross-functional stakeholders, and enables actionable business insights. Required expertise includes SAS, Python, Java, MATLAB, SQL and NoSQL databases, Hadoop-based data warehousing, MapReduce, Hive, and Presto. The position requires 5–8 years of related experience and a bachelor’s degree or equivalent experience.

AI Intern will assist with developing, testing, and improving AI and machine learning solutions using Python and data processing. Responsibilities include exploring generative AI, LLMs, prompt engineering, chatbots, RAG applications, and automation. The intern will research emerging AI technologies, troubleshoot solutions, and collaborate with the team while gaining practical experience.

Develop AI-driven analytics tools, including custom LLM agents, semantic layers, RAG pipelines, and production data pipelines. Deploy and monitor AI applications in AWS, optimize prompts, embeddings, and vector search, and validate agent responses with SQL. Collaborate with Product, QA, DevOps, Implementation Services, and engineering managers to translate business requirements into reliable AI features and align platform standards.