Top Machine Learning Jobs in India

Develop end-to-end software prototypes and research new technologies (e.g., blockchain), apply CS/math skills, work with distributed computing and polyglot codebases, explore ML/NLP/cryptography and game-theory related problems, and deliver quick prototypes.

Six-month NLP research internship focused on developing and applying machine learning and deep learning algorithms for natural language understanding in healthcare. Responsibilities include research, prototyping, experimentation, performance analysis, and presenting results.

Design, develop, and test diagnostic machine learning models (supervised and unsupervised) to improve output monitoring; build business cases to scale prototypes to production; collaborate with research, IT, and content experts in a multinational team to enhance product quality.

Design, develop, and implement advanced statistical and machine learning algorithms. Analyze disparate data sources to discover insights, build predictive models, prototype solutions, and collaborate with engineers to productionize models. Communicate findings to business and IT stakeholders and define system and functional requirements.

The Group Product Manager will drive WorkSpan's growth strategy focusing on Product-Led Growth and operational execution, leveraging AI capabilities and collaborating cross-functionally to enhance the user journey and revenue generation.

Lead development and optimization of astrodynamics algorithms for orbit determination, propagation, conjunction analysis, and prediction. Design, validate, and operationalize scalable, efficient SSA software; research ML/AI and numerical methods; mentor junior engineers; collaborate across software, data science, operations, and product teams; contribute to documentation, proposals, and publications.

Lead strategy and execution for fraud risk management across the platform. Develop detection and prevention frameworks, optimize transaction screening, partner with Compliance, Data Science, Engineering and Product, oversee KPIs and vendor costs, lead a risk strategy team, and pilot AI/LLM-driven tools to enhance detection while enabling business growth and regulatory alignment.

Lead advanced marketing analytics and measurement initiatives, build attribution, MMM and predictive models using structured and unstructured data, manage vendor relationships, present insights to senior stakeholders, and drive media test-and-learn and measurement roadmap execution across acquisition, engagement, and retention.

The IT Collaboration Platform Engineer will lead the design, development, and operation of cloud-based collaboration services, manage platform integrations, and ensure governance and compliance while driving innovation and optimization.

The Senior Analyst will independently deliver analytical work in procurement, using SQL, Python, and AI tools to improve business insights and decision-making.

Lead and manage a team of Software Engineers focusing on building tooling for internal teams at Nike, emphasizing collaboration and modern software practices.

Lead automation and analytics for internal audit: design Power BI dashboards, data analytics, and AI/RPA pilots to enhance SOX testing, control validation, anomaly detection, and continuous monitoring. Liaise with IT, business, and external auditors; coach team members; support remediation and audit transformation.

Own data quality and root-cause analysis for prediction issues: detect anomalies, trace sources, fix processes, automate QA/reporting with Python and SQL, maintain dashboards, and partner with Data Science, Engineering, Product, and Commercial teams to improve prediction performance and customer-facing analytics.

Lead design, build, and operation of data architecture, pipelines, and semantic layers. Manage sprint planning, DevOps metrics, data quality and monitoring, and MLOps enablement. Partner with stakeholders, coach engineers, track project plans, and ensure data availability, accuracy, and documentation.

Lead multi-stream data and AI projects, own project plans, budgets and risks, run governance meetings, coordinate Architects/Data Engineers/AI teams, apply Snap's delivery methodology, manage senior stakeholders, and oversee multiple concurrent client engagements to ensure timely delivery and client satisfaction.

Lead and manage a KYC operations team to deliver AML monitoring, onboarding, refresh processes, quality reviews, MIS reporting, and process improvements while liaising with Compliance and senior stakeholders to ensure regulatory adherence.

Develop, validate, optimize, and productionize machine learning models and predictive analytics solutions using Python, SQL, Databricks, and Microsoft Fabric. Responsibilities include data processing, feature engineering, statistical experimentation, model monitoring, workflow automation, agentic solution development, and technical documentation. The role partners with data scientists and regional stakeholders, communicates insights clearly, supports platform operations, and contributes to reliable end-to-end deployment across APAC.

Develop and integrate AI/LLM-powered applications, build backend services and REST APIs, work on ML models and prompt engineering, integrate third-party APIs, perform testing, debugging, deployment, documentation, and collaborate on live AI projects.

Design, deploy, and monitor auditable ML/AI models for financial‑crime detection; analyze large datasets, validate and improve data quality and rules, build dashboards and reporting, support model governance and regulatory compliance, and translate findings into business actions.

Own platform expertise for Accelerate by running live campaigns across major walled gardens, validating product features, translating agency workflows into product requirements, and guiding engineering on platform behaviors and automation.

Lead technical ownership of a production media-screening ML platform on AWS. Hands-on work designing, building, evaluating and shipping classical ML and LLM/GenAI models and services, owning reliability, cost, MLOps, roadmap and delivery, and partnering with product and external stakeholders to expand platform capabilities.

Lead AI/ML solution engineering for strategic healthcare accounts: drive architecture and reusable patterns, mentor solution leads, align productization and delivery, own pipeline and opportunity governance, engage executives, and shape AI-native standards and go-to-market pursuits.

Lead AI/ML strategy and delivery for healthcare and life sciences: oversee multiple client engagements, design scalable production-grade ML solutions, drive adoption of advanced AI (NLP, deep learning, LLMs, CV), ensure regulatory readiness, and act as executive sponsor for client relationships and portfolio growth.

Lead enterprise AI strategy, client executive engagement, and commercial growth; build and run a global AI engineering organization; drive Agentic AI, LLMs, cloud-native platform delivery, governance, and responsible deployment across healthcare and life sciences.

Build and productionize NLP and LLM models for search, summarization, classification, and answer generation. Design scalable Python inference services and retrieval pipelines, improve model accuracy through evaluation and error analysis, and integrate models with knowledge systems. Collaborate with Product, Engineering, and Applied Science teams to deliver reliable production systems, contribute to architecture and operational standards, and mentor others on NLP and model serving.