Top Machine Learning Jobs in India

Build and extend LLM evaluation harnesses, benchmark sweeps, dashboards, regression tracking, data-quality tooling, and training-run monitoring. Develop internal tools that improve research productivity while gaining experience in pretraining and post-training workflows. The role emphasizes strong Python coding, debugging, machine learning fundamentals, experimentation, and rapid learning, with opportunities to contribute to open-source medical models serving Indian-language healthcare use cases.

Build and operationalize production-grade forecasting and machine learning systems, including reliable pipelines, monitoring, data quality controls, custom Streamlit or embedded analytics applications, and workflow integrations. Partner with data scientists, BI, IT, and data teams to develop scalable insight products, improve observability, document solutions, and guide technical innovation for business stakeholders.

Leads research conceptualization, methodological and sampling design, instrument development, ethics governance, fieldwork, data quality, quantitative and qualitative analysis, and report writing. The role requires managing research projects, applying machine learning and statistical methods, using analytical software, communicating findings, and translating evidence for policymakers and other stakeholders. Prior experience in the skill ecosystem and strong analytical, reporting, presentation, and data-management capabilities are required.

Develop and deploy analytics, machine learning, and optimization solutions for semiconductor equipment and service operations. Analyze operational, field, diagnostic, and manufacturing data to identify productivity opportunities, build predictive models and decision-support tools, and drive measurable improvements in customer outcomes, revenue, quality, and efficiency. Collaborate with cross-functional teams from problem definition through deployment, adoption, monitoring, and continuous improvement.

Manage strategic digital payment initiatives across UPI, merchant acceptance, acquiring banks, and fintech partners. Track milestones, certifications, readiness, action items, and stakeholder commitments. Coordinate governance meetings and partner enablement while monitoring adoption, revenue, KPIs, and transaction trends. Prepare dashboards, business reports, CRM updates, presentations, and actionable insights for leadership and business teams.

Leads end-to-end delivery of complex Industrial AI and enterprise technology implementation projects, managing scope, schedules, budgets, financials, risks, governance, customer relationships, adoption, and value realization. Collaborates with technical and business teams to deliver measurable outcomes for industrial customers, while applying Agile methodologies and improving PMO practices. Requires technical fluency, commercial acumen, industrial-sector experience, and willingness to travel up to 25%.

Lead Engineer responsible for designing, evaluating, deploying, and monitoring AI, machine learning, business intelligence, and conversational AI solutions in insurance. The role covers statistical modeling, NLP, RAG pipelines, unstructured document processing, semantic data layers, cloud AI platforms, evaluation frameworks, governance, and production validation. It also requires translating technical findings and risks into business outcomes while addressing insurance processes, privacy, compliance, and ethical standards.

Lead enterprise Databricks architecture across AWS and Azure, designing secure, scalable Lakehouse, data engineering, analytics, machine learning, and AI platforms. Establish governance, data models, observability, architecture standards, migration roadmaps, disaster recovery, and cost optimization. Guide implementation teams, conduct architectural reviews, collaborate with executives and technical stakeholders, and mentor engineers through large-scale modernization initiatives.

Leads AI/ML engineering strategy, roadmap, team development, and delivery across search, recommendations, pricing, forecasting, fraud, and counterfeit detection. Sets architecture and model direction, drives experimentation, scales ML platforms and MLOps, and partners with product, data science, and business leaders. Owns hiring, performance, resource planning, prioritization, and measurable business outcomes while developing technical leaders and managers.

Leads hands-on LC-MS proteomics research, developing workflows from sample preparation through data analysis to address complex biological questions. Manages and mentors a scientific team, establishes data quality and reproducibility standards, troubleshoots technical challenges, and serves as a mass spectrometry subject matter expert. Evaluates automation, computational, AI/ML, and instrumentation technologies while communicating scientific strategies and recommendations across research programs.

Designs and delivers large-scale NVIDIA AI, cloud, deep learning, and accelerated analytics solutions. Supports field business development throughout the sales process, owns technical customer relationships, and advises on hardware, software, networking, storage, data center, and cloud architectures. Serves as a thought leader and subject matter expert for enterprise and supercomputing deployments, collaborating with customers, engineering, management, partners, OEMs, and distributors.

Drive enterprise sales across APAC as a technical pre-sales partner to Account Executives. Lead discovery, solution scoping, architecture design, product demonstrations, RFP/RFI responses, proof-of-concept execution, and customer enablement for geospatial and Earth observation platforms. Translate remote-sensing and analytics capabilities into business value, collaborate with product and engineering teams, and influence product roadmap priorities. The role is remote within APAC and requires 20–40% regional travel.

Leads business analytics initiatives by translating complex business problems into actionable insights. Partners with stakeholders, analyzes large datasets, develops Tableau dashboards and self-service reporting, applies AI and generative AI to analytics workflows, and presents recommendations to leadership. The role also mentors analysts, manages complex analytics projects, establishes best practices, and drives adoption of scalable analytics and automation solutions.

Leads end-to-end delivery of complex technical epics and engineering initiatives across multiple teams. Responsibilities include planning, estimation, dependency and risk management, Agile delivery, governance, stakeholder communication, milestone tracking, and executive reporting. The role requires strong software development lifecycle knowledge, engineering delivery experience, and the ability to translate technical issues into business outcomes. Experience with AI, Generative AI, Machine Learning, cloud, data, API, or enterprise-scale initiatives is advantageous.

Collect, clean, and analyze datasets; assist with developing, training, and evaluating machine learning models for classification, regression, and predictive analytics. Help optimize ML pipelines, document experiments and performance, collaborate on AI/ML research initiatives, and stay current with machine learning and generative AI developments.

Develops and deploys advanced analytics, machine learning, generative AI, and LLM solutions for enterprise business problems. Leads AI innovation, intelligent automation, RAG, agentic AI, and productivity initiatives across business functions. Builds scalable solutions using Databricks, Snowflake, cloud AI platforms, vector databases, and modern model deployment frameworks. Partners with product, data engineering, and solution teams, validates models, communicates findings, and mentors less experienced data scientists.

Supports the design, development, testing, deployment, and maintenance of cloud-based software in a microservices environment. Works with APIs, telemetry, structured and unstructured data, distributed systems, and ML/AI algorithms. Assists with automation, code reviews, refactoring, documentation, CI/CD, testing, and software integration while collaborating with product teams and stakeholders.

Leads global regulatory maintenance submissions, labeling updates, submission delivery plans, and health-authority procedures for pharmaceutical products. Provides regulatory strategy, documentation guidance, risk mitigation, stakeholder coordination, and scientific input across regions. Uses Veeva Vault and project-planning tools to manage deliverables, compliance, and documentation. The role also supports process improvement, knowledge sharing, and responsible use of AI tools while collaborating with global regulatory, clinical, manufacturing, safety, and commercial teams.

Leads global regulatory maintenance submissions, labeling updates, submission delivery plans, and regulatory procedures for pharmaceutical products across regions. Provides regulatory guidance, identifies risks, coordinates cross-functional stakeholders, manages documentation and tracking tools, and supports health authority interactions. Contributes to submission strategy, process improvement, coaching, and responsible use of AI tools. Requires strong regulatory expertise, project management, scientific understanding, and stakeholder leadership.

Leads global regulatory maintenance submissions, labeling activities, submission plans, health authority deliverables, and cross-functional regulatory projects for pharmaceutical products. Identifies risks, coordinates documentation and resources, supports compliance and process improvements, and provides regulatory guidance across regions. Requires substantial biopharmaceutical regulatory experience, strong project management and leadership skills, scientific knowledge, and familiarity with regulated markets, labeling, publishing, and digital tools.

Leads global regulatory maintenance submissions, labeling activities, submission planning, and health-authority procedures for pharmaceutical products. Provides regulatory guidance, manages risks, coordinates cross-functional teams, maintains delivery plans and documentation in digital tools, and supports compliance, process improvement, mentoring, and responsible AI adoption. Requires substantial biopharmaceutical regulatory experience, including licensing maintenance, labeling, publishing, and regulated-market operations.

Architects and delivers enterprise AI platforms, advanced analytics solutions, and scalable cloud-native architectures. Translates business requirements into secure, production-ready AI systems using Azure AI technologies. Defines patterns for generative AI, machine learning, intelligent applications, governance, MLOps, and operationalization. Collaborates with business, data, engineering, and platform teams, establishes architecture standards, evaluates emerging technologies, and guides AI initiatives from ideation through implementation.

Gather and define accounts payable and payments requirements across the invoice-to-pay lifecycle. Translate business needs into user stories, process flows, functional specifications, and acceptance criteria. Coordinate with stakeholders, engineering, architecture, QA, and delivery teams on ERP integrations, APIs, workflows, testing, compliance, reconciliation, and payment processing. Support prioritization, product delivery, automation opportunities, and platform modernization initiatives.

Lead data science and analytics for eBay’s View Item product area. Define success metrics, experimentation roadmaps, and measurement frameworks; apply statistical methods, machine learning, predictive modeling, and causal analysis to improve buyer experiences and revenue. Partner with Product, Engineering, and Business stakeholders to translate complex findings into product strategy and roadmap recommendations.

Develops and maintains business intelligence reports, dashboards, data models, and visualizations using Power BI, DAX, SQL, and enterprise data platforms. Extracts, transforms, validates, and analyzes data to generate actionable insights and support decision-making. Collaborates with stakeholders to gather reporting requirements, communicates findings, supports data governance, and improves reporting processes through automation and emerging technologies.