Top Machine Learning Jobs in India

Lead product strategy and execution for accounting and financial products serving automotive dealerships. Own the roadmap, customer research, product requirements, metrics, and end-to-end delivery of scalable B2B SaaS solutions. Partner with engineering, design, finance, compliance, marketing, analytics, and external stakeholders to improve financial workflows, including ledger management, reconciliation, settlement, reporting, payables, and receivables. Drive innovation, automation, product quality, and measurable customer impact.

Provides technical leadership for designing and building scalable, high-performance software systems. Leads complex engineering initiatives, designs distributed system components, partners with product and cross-functional teams, promotes engineering best practices, mentors developers, and resolves system-wide issues through root cause analysis. The role requires strong expertise in cloud-native development, containerization, scalability, fault tolerance, observability, and large-scale application delivery.

Own product strategy, roadmaps, feature development, and launches while collaborating with engineering, design, marketing, sales, and customer success. Gather customer and market insights, analyze competitive and usage data, prioritize initiatives, manage cross-functional dependencies and risks, define success metrics, and ensure product outcomes align with business objectives. The role also contributes to product management frameworks, tools, and best practices.

Build automated quality and evaluation frameworks for ML models, AI agents, LLM applications, prompt pipelines, RAG systems, and APIs. Develop evaluation datasets, regression suites, scoring pipelines, monitoring, guardrails, and model quality metrics. Investigate hallucinations, drift, behavioral regressions, and production issues while partnering with ML, data, product, and applied AI teams to improve reliability.

Own the strategy, roadmap, lifecycle, and outcomes for core CRM capabilities serving automotive dealerships. Drive customer adoption, engagement, conversion, and satisfaction through data-informed prioritization and scalable platform development. Lead cross-functional initiatives across Engineering, Design, Data, GTM, Marketing, and Customer Success; align stakeholders, manage dependencies, and mentor Product Managers. Translate customer and market insights into product decisions while balancing near-term execution with long-term platform strategy.

Own product strategies, roadmaps, and feature development for assigned product lines. Collaborate with engineering, design, marketing, sales, and customer success to launch solutions, gather customer feedback, analyze market and usage data, manage cross-functional dependencies, and define measurable outcomes aligned with business goals.

Own the end-to-end lifecycle and strategy for AI/ML-powered automotive service capabilities spanning scheduling, repair orders, parts, technician workflows, and invoicing. Partner with ML and engineering teams to prioritize use cases, define success metrics, design scalable systems, and establish product architecture standards. Use data to guide roadmaps and improve accuracy, adoption, workflow efficiency, and time savings across dealership operations.

Leads Medline’s India AI and Automation organization, strategy, governance, engineering practices, and enterprise solution portfolio. Builds multidisciplinary teams, drives AI adoption, oversees solutions from discovery through production and value realization, and ensures secure, scalable, reliable delivery. Guides capabilities spanning generative and agentic AI, machine learning, RPA, intelligent document processing, cloud platforms, MLOps, LLMOps, and automation while partnering with global stakeholders and business leaders.

Develop and deploy AI-driven product features using machine learning, NLP, and generative AI. Build end-to-end ML pipelines, integrate models into production microservices and APIs, analyze data, optimize model performance, and contribute to testing, documentation, technical reviews, and cross-functional product delivery.

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.

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.

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.

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.

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.

Leads the design, development, and deployment of production-ready AI and machine learning applications using LLMs, generative AI, cloud services, and on-premises pipelines. Responsibilities include developing scalable solutions, integrating deep learning and neural network capabilities, coordinating cross-functional teams, making technical decisions, and mentoring engineers.