Top Machine Learning Jobs in India

Leads clinical data science activities supporting pharmaceutical development programs. Performs exploratory analyses, visualization, statistical and machine-learning analyses across clinical, biomarker, real-world, and other data sources. Partners with cross-functional study teams to produce analysis-ready and submission-ready data, interpret findings, identify risks, and support development decisions. Ensures reproducible, traceable, regulatorily compliant deliverables while advancing automation, reusable workflows, AI/ML adoption, and team capability development.

Develops advanced data science and analytics solutions for HPE’s Finance and Corporate Strategy team. Responsibilities include defining analytics requirements, building statistical and machine learning models, integrating models into business processes, creating visualizations, and translating complex data into strategic business insights. The role requires collaboration with application developers and cross-functional teams, technical leadership, and the ability to connect mathematical models with measurable business outcomes.

Designs and develops scalable web-based automation platforms for manufacturing partners and business operations. Responsibilities include architecture, system integration documentation, troubleshooting, root-cause analysis, testing, production support, and delivery of enterprise software solutions. The role requires Python and broad backend, frontend, database, cloud, API, AI, and machine learning expertise, along with experience integrating AI capabilities into applications and workflows.

Directs product design for enterprise software, managing 8–10 designers or managers and overseeing product experience across mobile and desktop platforms. Partners with Product and Engineering on strategy, roadmaps, research, design systems, prototyping, usability, and execution. Mentors design teams, drives cohesive ecosystem experiences, and integrates AI, machine learning, and mobile technologies into workplace productivity solutions.

Supports data analysis, exploratory data analysis, metadata development, model building, validation, and selection. Contributes to researching and delivering data science solutions, preparing data, integrating and migrating information, and visualizing analytical insights. Works with business teams to understand objectives and apply basic statistical, machine learning, modeling, ETL, and data visualization techniques.

Analyzes and validates financial datasets, prepares data for exploratory analysis, develops and evaluates data science and machine learning models, and communicates insights through visualization. Supports metadata development, data integration, hypothesis creation, model selection, and delivery of analytics solutions that inform business decisions.

Designs, develops, operates, and scales secure data platforms, backend systems, pipelines, and distributed services. Provides technical leadership, reviews code, influences architecture, and serves as a subject matter expert in data processing, storage, and reliability. Leads enterprise adoption of AI-assisted engineering and SDLC automation by establishing governance, security controls, quality gates, and measurable outcomes. Collaborates across global teams and promotes scalable engineering practices and technologies.

As a Staff Data Scientist, lead complex data science projects, define analytical frameworks, and mentor team members to improve healthcare outcomes using advanced models.

Develops and integrates radiating components and passive radiation-pattern structures for multiband base-station antennas. Responsibilities include 3D and 2D electromagnetic simulation, prototype RF measurements, performance optimization, troubleshooting, technical documentation, and supporting the product lifecycle through mass production. The role collaborates with international cross-functional teams and requires relocation to Rosenheim, Germany.

Provides technical leadership for enterprise-scale data platforms and defines target-state architecture, engineering standards, technology roadmaps, and governance practices. Leads distributed data, application, integration, and streaming platforms; improves scalability, reliability, security, performance, and developer productivity. Partners with business and technology stakeholders, mentors senior engineers, drives AI-enabled engineering adoption, and oversees delivery across cloud, hybrid, workflow orchestration, observability, and data operations environments.

Serve as the first point of contact for customers, partners, and consultants. Research, troubleshoot, replicate, and test product issues; track incidents and provide resolution updates. Collaborate with Development, QA, and Consulting Services, occasionally support AI-related initiatives, and participate in a weekend rotation for 24/7 emergencies.

Leads validation strategy and automated testing for NAND-based SSD firmware across functional, performance, reliability, interoperability, and customer qualification domains. Develops test plans, frameworks, and continuous validation solutions; debugs NVMe and PCIe issues; analyzes quality risks; and drives cross-functional issue resolution. Partners with hyperscale customers, leads complex programs, mentors engineers, and applies AI, machine learning, and generative AI to improve test coverage, failure analysis, reporting, and validation productivity.

Join Celonis' 12-month Orbit Graduate Program as an Associate Value Engineer to support sales and customers by uncovering process problems, building proofs and AI prototypes on the Celonis platform, quantifying value, presenting to executives, driving change management, and feeding product insights back to engineering.

Lead AI, machine learning, and generative AI strategy and engineering for marketing-related domains. Design, deploy, and scale solutions using LLMs, RAG, AI agents, vector databases, and model fine-tuning across AWS, Azure, and GCP. Drive AI lifecycle automation through Agile, DevSecOps, and CI/CD practices; mentor teams; establish engineering standards and governance; and integrate AI capabilities into enterprise products and platforms.

Architects scalable ETL/ELT pipelines and enterprise lakehouse architectures; develops predictive machine learning models and production Generative AI, multi-agent, and RAG systems. Processes petabyte-scale data with Python, Scala, PySpark, Spark, and advanced SQL. Builds reusable features, optimizes distributed workloads, and applies software-engineering practices including testing, scalability, data quality, and continuous integration. Collaborates with research, engineering, and product teams while embedding ethical AI principles.

Lead end-to-end delivery of large, complex technical epics and engineering initiatives. Manage engineering teams, dependencies, risks, milestones, releases, governance, and stakeholder communications. Partner with engineering, architecture, product, business, and delivery teams to ensure predictable, high-quality outcomes. Facilitate Agile practices, resolve impediments, establish accountability, and translate technical information into business insights. Experience delivering AI, machine learning, cloud, data, API, or enterprise-scale initiatives is advantageous.

Lead the design, implementation, and optimization of mathematical models and production optimization engines for supply chain and strategic sourcing. Formulate MIP, LP, NLP, network flow, and heuristic algorithms; tune commercial solvers; integrate scalable solutions into a multi-tenant SaaS platform; validate model performance; and mentor engineering and research teams. Collaborate with product and engineering leaders to translate business constraints into reliable, production-grade software.

Develop predictive and prescriptive machine learning models, deploy and monitor them in production, and build scalable data services and MLOps workflows. Analyze complex datasets using Python, Spark SQL, Azure, Databricks, and MLflow; evaluate model performance and data drift; create technical architecture artifacts; conduct cost-benefit analyses; and communicate findings to technical and nontechnical stakeholders.

Lead a healthcare analytics and clinical configuration team, owning end-to-end auto-decisioning strategies for new client implementations. Translate business needs into scalable analytical frameworks, develop performance tracking and optimization initiatives, and deliver actionable insights from healthcare data. Partner with data governance and cross-functional teams to ensure explainable, responsible analytics. Manage team hiring, mentoring, coaching, and day-to-day operations while influencing product and business outcomes.

Performs global trade compliance screening for HPE transactions, including export controls, embargoes, restricted-party lists, and licensing decisions. The role analyzes performance metrics, backlogs, demand, and staffing needs; supports quality control, training, process improvement, automation, reporting, and stakeholder communication. It requires rotational AMS shifts, weekend coverage, and hybrid office collaboration.

Develop, test, deploy, and maintain production AI/ML models and systems for consumer-facing healthcare platforms. Build LLM applications, contribute across the AI lifecycle, manage cloud infrastructure using Infrastructure as Code, and improve reusable ML platforms. Collaborate with product and engineering teams, troubleshoot production issues, evaluate emerging technologies, and deliver scalable solutions involving NLP, computer vision, personalization, recommendation systems, and anomaly detection.

Owns enterprise KPI reporting, executive dashboards, finance and FP&A reporting automation, data governance, and scalable analytics products. Partners with global leaders and technical teams to translate business needs into data-driven and AI-enabled solutions. Leads AI integration, roadmap planning, stakeholder alignment, cross-functional projects, commercialization, and team mentoring while using Tableau, Power BI, SQL, and Python.

Owns a cybersecurity detection coverage area, including backlog prioritization, technical direction, detector design, implementation, code reviews, tuning, and performance measurement. The role improves detection fidelity, ATT&CK coverage, false-positive reduction, and regression prevention while translating threat intelligence, threat hunting, and SOC feedback into roadmap priorities. It requires deep expertise in at least one detection domain and hands-on detector-as-code development with version control and controlled deployment.

Build, train, evaluate, and deploy machine learning and LLM-based safety models, guardrails, and content moderation systems. Develop evaluation frameworks, adversarial testing, and low-latency inference solutions for high-throughput production workloads. Research defenses against jailbreaks, prompt injection, data leakage, and harmful content. Support AI governance, compliance, audit logging, and watermarking. Collaborate across engineering, product, legal, and trust teams while mentoring engineers.

Lead enterprise-scale AI/ML and mathematical optimization initiatives from business problem definition through production deployment. Design hybrid machine learning and optimization solutions using frameworks such as Pyomo, OR-Tools, Gurobi, CPLEX, PyTorch, and TensorFlow. Develop proof-of-concepts, decision systems, MLOps workflows, and production-ready architectures while collaborating with engineering, product, business, and leadership teams. Preferred expertise includes quantum optimization, reinforcement learning, generative AI, healthcare analytics, data platforms, and responsible AI.