Top Machine Learning Jobs in India

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.

Lead business analysis and product requirements for Learning & Talent platforms. Capture and groom epics, features, and user stories; develop process and data models; liaise with stakeholders, delivery, and data engineering teams to ensure value-driven, compliant agile delivery.

Lead design, implementation, testing, deployment and maintenance of distributed software services for Express Logistics. Ensure engineering best practices, high-quality code, and DevOps automation. Define technical direction and roadmap, hire and manage a development team, work in Agile, and deliver scalable, secure solutions incorporating ML, geolocation, and cloud technologies.

Design, optimize, and deliver enterprise AI/ML solutions on Azure Databricks and cloud platforms. Lead technical teams, implement streaming (Kafka/Spark Streaming) and database solutions, build CI/CD pipelines, adopt GenAI/LLM/RAG frameworks (LangChain/LangGraph/LangFuse), ensure codebase impact analysis, and collaborate with onshore stakeholders.

Design, build and maintain AI/ML and data engineering solutions using Databricks, streaming (Kafka/Spark), LLM/RAG frameworks. Ensure code quality, CI/CD, Agile processes, mentor engineers, and collaborate with US business hours support.

Design, develop, and deploy GenAI/agentic solutions and RAG-based applications. Build backend services and integrations using Java/Spring Boot or Python. Work with vector databases, prompt engineering, cloud (AWS/Azure), observability, and ensure security, privacy, and responsible-AI practices while collaborating with stakeholders.

Support data ingestion and pipeline reliability for marketing data feeds; run and validate ML models for trend detection; test tokenisation/anonymisation for regional compliance; maintain dashboards and front-end interfaces; manage AI briefing intake and map historical campaign data; automate CSV macros and tune generative AI prompts for paid media workflows.

Design, develop, and deploy scalable AI/ML and LLM-powered solutions. Build large-scale data architectures, pipelines (Kafka/Flume/Spark), and big-data environments on-prem/cloud. Develop agentic AI systems, optimize model performance and reliability, enable researcher data access, collaborate cross-functionally, and contribute to research, PoCs, and production-grade deployments.

Develop, test, and maintain Python applications and scripts; assist with REST APIs, backend services, automation, data processing, reporting, and analytics. Participate in debugging, code reviews, documentation, and cross-functional collaboration while learning cloud, AI, data engineering, and emerging technologies.

Develop, test, and maintain Python applications and scripts; assist with REST APIs, backend services, automation, data processing, reporting, and analytics. Participate in debugging, code reviews, documentation, and cross-functional collaboration while learning cloud, AI, data engineering, and modern software development practices.

Build and maintain Python-based supply and demand models and price forecasts for crude and refined products in EMEA. Collect and manipulate large datasets, perform quantitative and qualitative analysis, produce client-facing reports, Power BI dashboards and PowerPoint presentations, and support business development and stakeholder engagement.

Develop and deploy production-ready AI applications using cloud or on-premises pipelines. Apply generative AI, conversational AI, deep learning, neural networks, chatbots, and image processing. Lead technical decisions, collaborate across teams, mentor junior engineers, manage team delivery, and evaluate emerging AI technologies for adoption.

Design and deploy production-ready AI applications using cloud AI services and on-premises infrastructure. Integrate generative AI, large language models, deep learning, neural networks, conversational agents, and image processing. Lead scalable and reliable AI model development, optimize production pipelines, collaborate with stakeholders, make team-level technical decisions, and mentor junior engineers.

Designs, builds, and operates production full-stack applications with embedded agentic AI and LLM capabilities. Responsibilities include developing React-based front ends and Python or Node.js back ends; integrating agent orchestration, RAG pipelines, voice and avatar features; building secure multi-tenant APIs; managing relational and NoSQL data layers; implementing asynchronous processing; and deploying containerized systems on cloud infrastructure. The role also applies enterprise security, automation, CI/CD, and multi-cloud practices.

Develop and deploy production-ready AI applications using cloud or on-premises pipelines. Integrate generative AI, deep learning, neural networks, chatbots, and image processing capabilities. Manage model deployment, monitoring, lifecycle management, reliability, and scalability. Collaborate with stakeholders to translate requirements into technical solutions, troubleshoot production pipelines, optimize model performance, and guide junior team members.

Develop and operate production-ready full-stack applications featuring agentic AI, LLMs, RAG, and multi-agent workflows. Build React or Next.js front ends with Python or Node.js back ends, secure REST APIs, asynchronous processing, and relational or NoSQL data layers. Deploy containerized systems on cloud infrastructure using Docker and Kubernetes, integrate Azure or equivalent cloud services, and implement authentication, RBAC, encryption, SSO, and audit logging. Collaborate with architecture and AI leads to deliver scalable enterprise products and reusable technical innovations.

Design, build, and operate production full-stack applications featuring agentic AI, LLMs, RAG pipelines, and multi-agent workflows. Develop React-based front ends and Python or Node.js back ends, secure APIs, asynchronous services, data layers, and cloud-native deployments. Integrate AI orchestration, voice and avatar capabilities, authentication, RBAC, and enterprise security controls. Collaborate with architecture and AI leads, create automation tooling, and deliver scalable products across cloud platforms.

Design, train, fine-tune, evaluate, and deploy production machine learning models for cybersecurity detection. Build efficient transformer and LLM systems optimized for latency, throughput, memory, cost, and reliability. Develop scalable inference infrastructure, data pipelines, and labeling workflows; collaborate with security researchers and product teams; monitor production models and improve detection quality and robustness.

Design, prototype, and deploy AI/ML and computer-vision solutions for production factories. Perform feasibility studies, collaborate globally with research teams, support operational excellence, and integrate systems with existing hardware to improve production processes.

Design and deploy production-ready AI applications using LLMs, generative AI, cloud or on-premises pipelines, deep learning, neural networks, chatbots, and image processing. Lead model integration, optimize application performance, coordinate stakeholders, make cross-team technical decisions, and mentor team members.

Design, build, and operationalize ML and Generative AI solutions (LLMs, RAG, agents). Perform EDA, predictive modeling, deploy MLOps pipelines, develop APIs and automation, run performance testing, monitor models, and collaborate with stakeholders to deliver enterprise AI solutions.

Provide 24x7 cloud infrastructure support for public/private cloud services (AWS), monitor availability, manage incidents to meet SLAs, assist production deployments, ensure security/compliance for fintech, and evangelize technical concepts across teams.

Lead Verizon’s enterprise AI Control Plane and AI governance ecosystem. Architect automated, code-driven compliance guardrails, integrate AI platforms with safety and compliance systems, and operationalize evaluation frameworks for autonomous and customer-facing agentic systems. Partner with executive Legal, InfoSec, and Responsible AI stakeholders, manage AI/ML governance KPIs and GPU-related FinOps, direct AI engineers and scientists, and drive innovation through advanced evaluation methods, intellectual property, and patents.

Lead an 8-10 engineer team owning courier systems (dispatch, matching, pricing, ETA). Set technical direction, drive data-driven decisions and experiments, partner with data science, ensure reliability and scalability of low-latency distributed systems, and own hiring, performance, and operational excellence.