Top Machine Learning Jobs in India

Build and productionize machine learning, LLM, and AI solutions with Data Science teams. Design industrialized data-processing pipelines, implement MLOps and LLMOps frameworks, manage model lifecycles, and develop production Python, ML, and cloud solutions using Azure, GCP, Databricks, Spark, and PySpark. Gather requirements, estimate work, document solutions, present results, and support technical best practices.

Owns product strategy and execution for programmatic ad monetization, including demand prioritization, auctions, pricing, ad delivery, partner integrations, yield optimization, analytics, new ad formats, and AI-driven monetization capabilities. Partners with Engineering, Data, Business, Operations, and external providers to launch scalable products and improve revenue efficiency, ad quality, and user experience.

Own full-chip physical verification and signoff for advanced GPU and SoC designs from block assembly through tapeout. Responsibilities include DRC, LVS, antenna verification, 3D die-stacking validation, advanced-node signoff, AI-powered automation, flow development, and cross-functional design closure. Requires expertise in physical design verification, VLSI concepts, signoff tools, and RTL-to-GDSII implementation processes.

Designs, implements, validates, and deploys complex statistical and machine learning models to solve business problems. Partners with architects and domain experts to enable data flow, prototype solutions, and productionize models. Mentors junior staff, communicates results to stakeholders, and advances team capabilities through knowledge sharing.

Analyzes recurring operational issues and data to identify root causes, then translates business rules into AI requirements, prompts, validation checks, and test cases. Collaborates with Product, Engineering, AI/ML, and Implementation teams to improve extraction, mapping, classification, and automation workflows. Evaluates LLM outputs, identifies failure patterns, builds reusable AI workflows and evaluation datasets, measures accuracy and efficiency improvements, and documents reusable solutions.

Design and deploy production-grade Generative AI and Agentic AI solutions for claims processing, underwriting, customer service, and decision automation. Analyze structured and unstructured data, apply machine learning and NLP techniques, collaborate with clients and stakeholders, identify process improvements, lead working sessions, and support issue resolution. The role requires strong data science fundamentals, LLM and AI orchestration experience, client communication skills, and the ability to work across global teams.

Leads strategy, innovation, customer discovery, execution, and go-to-market activities for Nielsen’s Ad Intel product portfolio. The role defines multi-year roadmaps, identifies monetization opportunities, partners with engineering and data science teams, launches products from 0-to-1 through enterprise scale, and shapes pricing, positioning, partnerships, and sales enablement.

Build and own production machine learning models and data pipelines for process intelligence, including task detection, workflow segmentation, behavioral analysis, and process discovery. Responsibilities include feature engineering, model evaluation, experimentation, large-scale multimodal data processing, data quality monitoring, production issue diagnosis, and cross-functional communication with engineering, product, customer success, and solutions teams.

Develop automated forecasting and valuation solutions for acquisition marketing investments. Extract, transform, and analyze large datasets using SAS, SQL, and Python; build quantitative models; apply statistical modeling, machine learning, and advanced analytics; optimize marketing investments; and implement data quality, validation, risk controls, and workflow automation. The role partners with cross-functional stakeholders and may provide team leadership and thought leadership.

Leads the design, development, and production deployment of enterprise AI, Generative AI, Agentic AI, NLP, LLM, and RAG solutions. Builds AI assistants, copilots, chatbots, data pipelines, and MLOps frameworks; optimizes models and inference; and establishes governance, monitoring, and responsible AI practices. Provides solution architecture leadership, mentors technical teams, collaborates with stakeholders, and drives AI transformation initiatives while remaining hands-on.

Lead Workday integrations, packaged application implementations, custom Workday Extend app development, and cross-functional product launches. Configure Workday Studio, EIB, and Core Connector solutions; manage application lifecycles through production deployment; establish platform best practices; and support adoption through team development. Apply HR, payroll, and compensation domain knowledge while exploring AI, machine learning, and intelligent automation capabilities.

Leads Workday integrations and packaged application implementations, including customization, module implementation, enterprise application integration, and development using Workday Studio, EIB, Core Connector, and Workday Extend. Builds and deploys custom Workday applications, coordinates cross-functional product launches, establishes platform best practices, and supports adoption. Applies expertise in HR, payroll, and compensation while exploring AI, machine learning, and intelligent automation capabilities.

Supports sales-force effectiveness through data cleaning, access governance, KPI reporting, performance trend analysis, and migration from Excel tracking to Power BI dashboards. The role also provides user support, onboarding and training assistance, documentation, process standardization, and continuous improvement across regional sales entities.

Lead Engineer responsible for developing end-to-end machine learning, generative AI, conversational AI, RAG, business intelligence, and analytics solutions. The role covers experimentation, evaluation, monitoring, deployment, semantic modeling, unstructured document processing, cloud AI platforms, and enterprise AI governance. It requires applying advanced NLP and GenAI techniques to insurance use cases while communicating technical findings, risks, and business outcomes to technical and non-technical stakeholders.

Leads delivery of enterprise conversational AI platforms, including chatbots and voice bots. Oversees Agile execution, technical architecture, cloud-native engineering, CI/CD, Terraform, Responsible AI, and AI adoption. Partners with product, security, architecture, and business teams while managing risks and executive communication. Mentors engineers and technical leads, supports hiring and performance management, and ensures scalable, secure, compliant, and operationally excellent solutions.

Lead statistical activities for Phase I–IV clinical trials from protocol design through results reporting. Provide biostatistical expertise for clinical development plans, protocols, analyses, safety reporting, regulatory inquiries, and publications. Collaborate with cross-functional teams, oversee internal programmers and CRO vendors, use SAS and R for statistical analyses and quality control, apply machine learning methods, and mentor junior statisticians.

Serve as a technical expert throughout enterprise sales cycles, partnering with account executives and customers to identify requirements, demonstrate SingleStore solutions, develop and deploy proofs of concept, lead technical evaluations, tune queries, conduct load testing, and support account expansion and channel partnerships. The role requires expertise in distributed databases, data platforms, AI and ML, cloud and containerized environments, programming, and enterprise technology sales.

Leads the design, development, deployment, and optimization of AI, generative AI, and large language model solutions. Builds production-ready systems in Python, conducts ML experiments, integrates GenAI into platforms, and advances MLOps practices. Collaborates with product, engineering, and business teams, ensures responsible AI governance and regulatory compliance, communicates technical findings, and mentors AI engineers and scientists.

Lead design and delivery of agentic AI and LLM orchestration solutions: define reference architectures, build reusable GenAI/RAG components, operationalize agentic workflows, ensure security, observability, and cross-team adoption.

Develops and governs AI capabilities for Finance by identifying use cases, designing automation and AI-enabled workflows, validating data and models, integrating AI with analytics platforms, and ensuring controls, compliance, monitoring, and human oversight. The role bridges Finance and IT, supports transformation programs, creates prompts and user enablement materials, manages stakeholders, and coordinates project governance. It requires finance process expertise, machine learning knowledge, analytics proficiency, project management skills, and willingness to travel.

Lead and grow a team of applied scientists developing scalable AI/ML applications. Drive research and product roadmaps, customer-facing delivery, technical architecture, and full machine learning lifecycle execution. Partner with product, engineering, business, and customer success teams to align priorities and deliver measurable business impact. Oversee advanced deep learning techniques, cloud-native solutions, quality standards, mentoring, and cross-functional collaboration.

The IT GRC Risk and Automation Analyst manages enterprise GRC platforms, integrated risk tools, automated reporting, API integrations, evidence-collection workflows, and continuous compliance monitoring. The role conducts third-party security assessments and translates technical risks into business impacts. It requires expertise in security, privacy, AI governance, and risk frameworks, including NIST CSF, ISO 27001, SOC 2, GDPR, NIST AI RMF, ISO 42001, and the EU AI Act.

Build and deploy classical machine learning and GenAI solutions, including forecasting, recommendations, RAG, LLM applications, and agentic workflows. Prepare and validate large-scale datasets, evaluate models, monitor production systems, and optimize reliability and cost. Create dashboards and translate technical findings into actionable recommendations for customers and stakeholders. Collaborate with product, engineering, and business teams throughout the solution lifecycle.

Design and deliver scalable Generative AI solutions using LLMs, RAG, AI agents, vector databases, and multimodal models. Lead cloud, MLOps, security, governance, and architecture strategies across AWS, Azure, and Google Cloud. Translate business needs into technical solutions, support client workshops and pre-sales, present strategies to executives, and mentor data science, ML engineering, and software teams.

Annotate and categorize images, advertisements, documents, and other content for machine learning datasets. Use custom software to label elements, assign text tags, draw bounding boxes and polygons, perform object detection and image classification, curate images, and document bugs or errors. The role requires accuracy, attention to detail, timeline adherence, computer proficiency, and the ability to work independently and collaboratively.