Top Machine Learning Jobs in India

Lead industrialization and site construction for a new turbine and generator product line: manage teams, ensure EHS and ISO compliance, drive lean and smart-manufacturing integration, oversee quality, commissioning, scheduling, cost control, and new-product qualification to meet production targets.

Enable analysis, modeling, and optimization by building analytic information products. Conduct R&D and deploy advanced techniques (machine learning, text mining, statistical analysis) on complex data (VLDB, multi-structured, big data). Familiar with ML DevOps lifecycle and agile practices. Work independently or in teams; may manage associates and deliver solutions for revenue, risk, operations, compliance, portfolio management, and research.

Implement, validate, and deploy ML and rule-based models for industrial clients. Perform tag mapping from P&IDs/PFDs and control systems, ensure model quality through validation and QA, collaborate with Project Managers and Technical Consultants, automate model-building tasks, and provide feedback to improve product and deployment processes.

Lead and build massively scalable software and systems; architect low-latency infrastructure; address cybersecurity concerns; apply machine learning with financial engineering to solve complex client problems and drive new product capabilities.

Lead requirements gathering, process analysis, and documentation for Legal IT systems. Support design, testing, training, stakeholder communication, and project delivery. Identify automation/AI opportunities and monitor KPIs to improve contract lifecycle efficiency and compliance.

As a Senior Backend Engineer, you will design and maintain scalable software solutions, embrace innovation with AI tools, and ensure high-quality engineering practices.

Design, build, and automate secure AWS infrastructure and CI/CD pipelines. Ensure availability, performance, scalability, and compliance (PCI‑DSS, HIPAA, SOX, GDPR, CCPA). Manage releases/configuration, container and API security, serverless solutions, security automation/ML, monitor systems, and publish CICD best practices.

Design, build, and maintain backend APIs and services for a real-time supply chain visibility platform. Own system architecture, real-time event ingestion, data normalization, ML integration, monitoring, and production debugging while collaborating with Product and Data Science to deliver scalable, high-availability features.

Use customer, bureau, transactional and repayment data to build and monitor credit risk features and predictive models. Design and evaluate credit policy experiments, support portfolio analytics (vintage, roll-rates, migration), implement model/feature monitoring, and collaborate with data engineering on pipelines, data quality, and governance.

Lead end-to-end data science projects: collate, clean, feature engineer, design and train models (predictive, prescriptive, NLP), validate results, mentor juniors, manage client deliverables, and translate business needs into analytical solutions.

Lead and oversee GAN solutions across the organization, drive high-impact GAN initiatives, ensure alignment with organizational goals, and provide executive-level strategic guidance.

Design, build and validate credit risk scorecards (application, behavioral, collections). Extract, clean, and preprocess data using SQL; develop ML models in Python. Monitor model performance, backtest and recalibrate to meet regulatory standards. Collaborate with business teams and document modeling processes for governance and audit.

Work on AI/ML/Deep Learning models for new use cases, gain exposure to real-world industry data, produce written artifacts (articles), participate in guided knowledge-transfer sessions, and potentially earn recommendation or a pre-placement offer based on performance.

Lead and define data science strategy for Health and Life Sciences operations, identify opportunities, manage a data science team, deliver analytics and ML solutions, build dashboards and KPIs, partner with executives, manage budgets and timelines, and ensure data governance and compliance.

Lead retail-focused data science to integrate offline store data with online channels. Build AI/ML and predictive models for demand forecasting, inventory and staffing optimization, and measure in-store marketing impact. Incorporate external factors (location, weather, events) to predict store performance and drive business decisions.

Design, build, and deploy end-to-end ML/DL and statistical models to solve business problems (claims, underwriting, risk, servicing). Engage clients, lead stakeholder sessions, diagnose process inefficiencies, implement scalable pipelines, and communicate technical insights to deliver client-ready analytics solutions.

Lead development, validation, and monitoring of real-time fraud detection models using ML and deep learning. Perform EDA, root-cause analysis, optimize model performance and explainability, collaborate with fraud strategy and operations, and produce technical documentation and actionable insights for leadership.

Perform data analysis for banking risk operations: analyze large datasets, identify trends and root causes, track KPIs, ensure data integrity, translate findings into business recommendations, and develop/automate dashboards and reports to support decision-making.

Design and build AI-powered applications integrating OpenAI models (GPT, Codex, DALL·E). Define use cases with cross-functional teams, implement prompt engineering and fine-tuning, ensure responsible AI practices, monitor and optimize model performance, write maintainable code, participate in reviews, troubleshoot integrations, and stay current with AI research.

Lead analytics projects from planning through delivery: manage teams, gather stakeholder requirements, build SQL/Python solutions, apply ML methods, create dashboards and presentations, and mentor staff to drive data-driven decisions.

Lead large-scale data management, exploratory data analysis, and feature engineering using bureau data for credit risk modeling. Build, optimize, and document machine learning models, automate EDA/feature transformation in Python, and present data-driven insights to technical and non-technical stakeholders.

Perform marketing analytics for P&C insurance campaigns: translate business questions into analytical solutions, program end-to-end in Python/SAS, run EDA, build and QC statistical and machine learning models, and generate campaign performance reports to inform decision strategy.

Lead operational assessments and transformation engagements: redesign processes, drive continuous improvement (Lean/Six Sigma/Kaizen), implement digital automation (RPA, GenAI, ML), leverage advanced analytics and data visualization, manage stakeholders and change, deliver project governance, and measure impact via KPIs.

Lead end-to-end operational transformation engagements: assess operations, design process redesigns, drive RPA/AI and digital initiatives, deliver data-driven insights, manage cross-functional teams, stakeholder engagement, change management, and measure KPIs to ensure sustainable results.

Collect, clean, transform and analyze large datasets; perform feature engineering, train, evaluate and fine-tune ML and NLP models; deliver analytics-driven insights; manage client deliverables and translate business requirements into analytical solutions.