Top Machine Learning Jobs in Delhi

Sell SailPoint's IGA identity security SaaS to $5B+ enterprises by engaging C-level stakeholders, managing long sales cycles with POCs/RFPs, collaborating with GSIs and channel partners, developing account and opportunity plans, and meeting quota using Challenger methodology.

Build, tune, deploy, and validate machine learning models for fraud detection, risk scoring, and customer analytics in the BFSI and credit card domain. The role involves performance and stability testing, documentation, troubleshooting data and model issues, communicating findings to varied stakeholders, managing analytical projects independently, and staying current with emerging AI and ML techniques.

Lead presales Solution Architect for UiPath Intelligent Automation. Run discovery workshops, design scalable enterprise automation, deliver demos/PoCs, produce technical proposals, define governance and integrations, and mentor teams while driving CoE and adoption.

Leads the architecture, development, and productionization of AI/ML and GenAI solutions for healthcare operations. Responsibilities include building clinical and claims data pipelines, developing LLM and RAG applications, implementing MLOps and LLMOps, ensuring HIPAA-compliant governance, translating business needs into technical roadmaps, communicating with stakeholders, and mentoring engineers.

Lead design and implementation of autonomy and edge-AI software for air, land, and sea platforms. Develop tactical autonomy algorithms, integrate software with hardware, deploy and field-test capabilities, analyze mission data, mentor teammates, collaborate cross-functionally, and support customers and defense programs.

Leads the engineering organization and oversees software delivery, system architecture, platform security, AI adoption, and engineering excellence. Scales high-traffic e-commerce platforms, integrates AI technologies into products and development workflows, ensures compliance, and partners with Product, Operations, founders, and senior leadership. The role also includes team development, mentoring, cross-functional collaboration, and ownership of the engineering and AI roadmaps.

Designs, develops, tests, deploys, and maintains AI-enabled software in cloud and microservices environments. Builds RESTful APIs and distributed systems, applies ML/AI algorithms to large structured and unstructured datasets, contributes to automation and CI/CD, reviews and refactors code, and collaborates with product and engineering teams. Requires expertise in multiple programming languages, public-cloud platforms, databases, containers, software architecture, Agile practices, and full software delivery lifecycles.

Designs, develops, tests, deploys, and maintains software in distributed microservices environments. Builds RESTful APIs, cloud-based applications, automated tests, and large-scale systems using structured and unstructured data. Collaborates across teams on integration, code reviews, refactoring, documentation, and deployment. Applies machine learning and AI algorithms, works with multiple cloud platforms and databases, and contributes throughout the software delivery lifecycle using Agile, Lean, CI/CD, and DevOps practices.

Design and build reinforcement-learning training environments and diverse tasks to evaluate and improve LLM agents; iterate rapidly on task designs from customer feedback, deliver high-quality outputs with minimal supervision, and maintain PST overlap for collaboration.

Lead research on improving AI agent training data and evaluations. Manage and mentor research engineers, define research direction, design rigorous experiments, validate data quality, diagnose reward and grading failures, and translate findings into scalable tools, workflows, and standards. Collaborate with engineers, domain experts, and vendors while communicating technical conclusions and tradeoffs clearly.

Build and maintain mission-critical wealth management systems for SMB clients. Design data pipelines, ETL/ELT processes, data models, warehouses, analytics, and reporting solutions using AWS, Snowflake, Mage AI, and other big data tools. Collaborate with data scientists and analysts on predictive models, statistical analyses, and machine learning algorithms. Develop software with Next.js and Python while working closely with the team and clients on financial-domain challenges.

Conduct applied AI and machine learning research for Microsoft products. Design, train, fine-tune, and evaluate models including LLMs and SLMs; analyze large datasets; build scalable prototypes; design experiments and benchmarks; optimize model performance; and collaborate with researchers, engineers, and product teams to deploy research innovations. Document findings through technical reports, publications, patents, or open-source work.

Leads biomedical data science for clinical and health research, integrating clinical, observational, EHR, genetic, wearable, electrophysiological, and video data. Develops signal-processing methods, machine-learning models, probabilistic and time-series analyses, scalable data pipelines, and reproducible workflows. Validates clinically relevant models, supports disease phenotyping and predictive modeling, ensures compliance with pharmaceutical and healthcare standards, and communicates technical findings to clients and stakeholders.

PhD intern will research and prototype AI techniques for Azure SQL Database, analyze algorithms on large datasets, build scalable ML systems, collaborate with researchers and product teams, and contribute to technology transfer and publications.

Reviews financial services regulations and exchange rulebooks, assesses regulatory changes, and maintains auditable review records. Validates machine-learning topic classifications, provides labelled feedback, and helps maintain regulatory taxonomies. Compares platform rulebooks with authoritative sources to identify structural errors, missing provisions, duplication, and versioning issues. Performs quality assurance against development requirements and collaborates with product, engineering, and software teams to process new regulations and improve the service.

Data Science Interns use machine learning, statistics, experiments, algorithms, and data exploration to solve business and product problems. They formulate analytical approaches, interpret and validate results, collaborate with stakeholders, produce actionable insights, and help improve Microsoft products and customer experiences through peer review and iterative analysis.

Lead technical direction and delivery for a core NIRA backend domain: design and ship high-volume web APIs, own architecture for distributed and queue-based systems, drive complex initiatives, mentor engineers, lead incident response, champion AI dev tools, and align cross-functionally to simplify systems and influence roadmap.

Analyze billions of user behavior, fund-flow, and market records using PySpark, Hive, Trino, and SQL. Build feature pipelines, investigate data anomalies, and develop and evaluate machine learning models for risk detection. Translate model performance into business metrics such as detection rate, false-positive rate, and latency while owning a project stream end to end.

Configure and optimize SAP IBP modules for demand, supply, inventory, and S&OP planning. Responsibilities include designing forecasting models, planning areas, key figures, master data, MEIO parameters, dashboards, alerts, workflows, and integrations with SAP ECC or S/4HANA through CI-DS. The consultant will support enterprise supply chain planning, constraint-based capacity evaluations, and cross-functional S&OP processes.

Design, develop, deploy, and scale production-grade machine learning and Generative AI applications. Build LLM-powered applications, intelligent agents, APIs, automation workflows, and ML pipelines using Python and FastAPI. Deploy systems with Kubernetes and cloud infrastructure, evaluate model performance, and optimize reliability, latency, accuracy, and cost. Collaborate with product and engineering teams while owning solutions from experimentation through production.

Develop quantitative models for cryptocurrency derivatives and spot markets, conduct statistical and econometric analysis, build pricing and risk models, and backtest systematic trading strategies. Collaborate with trading and product teams to validate and deploy models, monitor performance, analyze high-frequency data, and communicate research insights to leadership and trading desks.

Conduct experimental research in machine intelligence, develop and refine machine learning algorithms and models, apply research findings to existing AI systems, collaborate with engineers and researchers, publish results, and stay current with advances in machine learning.

Lead end-to-end SAP IBP solution architecture across demand planning, response and supply, optimizer, and order-based planning. Design data models, integrations, planning areas, optimization models, and SAP ecosystem architectures. Lead workshops, blueprinting, testing, go-live, and hypercare while engaging executive stakeholders. Mentor consultants, coordinate cross-functional teams, develop practice assets, and support continuous improvement across SAP IBP implementations.

Conduct STEM and emerging-technology workshops, demonstrations, presentations, and hands-on learning activities for school audiences. Research innovation trends, develop educational content and programs, demonstrate robotics, AI, ML, and maker-space concepts, and collaborate with internal teams. The role requires strong public speaking, mentoring, communication skills, a personal laptop, bilingual English and Hindi proficiency, and willingness to travel for school visits and workshops.

Research and develop quantitative signals from high-frequency equity market data. Responsibilities include feature engineering, exploratory data analysis, simulation and backtesting, validation across markets and regimes, signal implementation, and collaboration with quantitative researchers and engineers. The role also involves applying machine learning and deep learning to market microstructure, building production-quality low-latency code, exploring data sources, and developing tools to automate software development, testing, and deployment workflows.