Top Machine Learning Jobs in Delhi

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.

Lead enterprise technology and records management initiatives across business and technology teams. Responsibilities include stakeholder engagement, program and portfolio management, Agile delivery, records retention policy implementation, digital data purging, retention-by-design deployments, AI-enabled enterprise capabilities, data governance, and third-party RecordPoint relationship management.

Drive adoption of Databricks Genie across a regional territory through technical strategy, executive engagement, workshops, presentations, demos, and customer-facing collateral. Partner with Account Executives, GTM, Product, Engineering, GSIs, and consulting firms. Architect governed analytics solutions spanning semantic models, warehouses, metric layers, Lakehouse, and AI ecosystems; address governance and security concerns; resolve complex technical escalations; and provide roadmap feedback.

Design, develop, and optimize secure, scalable cloud data platforms and ETL/ELT pipelines across AWS, Azure, and GCP. Write Python and SQL, implement data governance and quality controls, optimize performance and costs, and support analytics and BI layers. Contribute to architecture, pre-sales demos, proofs of concept, and client discussions. Mentor junior developers, conduct code reviews, and collaborate with architects, QA, DevOps, and business stakeholders.

Owns data collection and annotation operations, defines product specifications and user flows, and supports apps and internal tools through delivery. Manages field teams, implementation partners, pilots, feedback loops, product metrics, and deployment improvements. The role also contributes to grant and partnership work while collaborating with engineering and ML teams. Candidates need product or delivery experience, technical fluency, data comfort, field deployment exposure, and familiarity with AI or ML products and data protection.

Own product initiative delivery from kickoff through release, coordinating Product, Engineering, Data, and Solutions. Track dependencies, risks, blockers, scope changes, timelines, testing, handoffs, and launch readiness. Maintain accurate project status and documentation while ensuring execution remains aligned with product intent. This hands-on role requires technical fluency, cross-functional communication, strong ownership, and daily overlap with an Asia-based technology team.

Lead complex technology projects from planning through closure, managing scope, budgets, schedules, resources, dependencies, risks, governance, and stakeholder communication. Coordinate engineering, product, QA, operations, consulting teams, vendors, and clients. Manage Agile execution, documentation, compliance, executive reviews, and government or public-sector engagements. Mentor teams, improve delivery processes, support pre-sales and proposals, and ensure predictable, high-quality project outcomes.

Lead the development and scaling of AI-powered EdTech and SaaS products. Define product requirements, technical direction, roadmaps, and AI/ML feature priorities; guide architecture across cloud, APIs, microservices, databases, and analytics. Collaborate with educators and stakeholders to develop personalized learning, assessment, content-generation, recommendation, and analytics solutions. Lead multidisciplinary teams, establish responsible AI practices, manage pilots, and improve product development, deployment, testing, and operational processes.

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.

Design and build production software and embedded machine learning systems for public-sector clients. Responsibilities include backend and API architecture, data pipelines, model development and evaluation, production reliability, security and privacy, cloud and constrained infrastructure decisions, offline-first systems, client collaboration, technical scoping, handover, and mentoring. The role remains hands-on, requiring weekly software development and regular model training while setting engineering standards for the organization.

Leads research conceptualization, methodological and sampling design, instrument development, ethics governance, fieldwork, data quality assurance, quantitative and qualitative analysis, and report writing. Responsibilities include using R, Python, Stata, or similar tools; applying machine-learning techniques; analyzing microdata; visualizing findings; and translating evidence into policy-oriented communications. The role also requires stakeholder engagement, presentations, project management, and prior experience in the skill ecosystem.

Build and deploy machine learning models on large healthcare datasets, extract actionable insights, develop scalable data processing systems, collaborate with cross-functional teams, and communicate findings to stakeholders to improve patient outcomes.

Designs, experiments with, deploys, monitors, and evaluates machine learning models. Requires full-stack development involving Python, React, AWS, APIs, and SQL, along with statistical and probabilistic foundations. The role supports AI/ML initiatives in a consulting environment serving financial services and energy clients.

Deploys at utility customer sites to drive product adoption, observe platform usage, document UI, functionality, and analytical issues, and manage feedback with engineering. Builds relationships across customer organizations, presents demos to senior utility leaders and IAS officers, validates platform outputs against customer data, closes the loop on shipped features, and identifies expansion opportunities. Requires strong writing, technical curiosity, ownership, and comfort using AI tools.

Leads and mentors full-stack engineers while remaining hands-on with scalable web, backend, cloud, and AI-enabled application development. Owns architecture through deployment, builds CI/CD pipelines, integrates LLM and RAG capabilities, and ensures application security, performance, availability, and reliability. Responsibilities also include code review, engineering standards, cloud optimization, production monitoring, troubleshooting, cross-functional collaboration, and delivery of business-critical features.

Build and deploy end-to-end production machine learning systems for public-sector institutions. Responsibilities include model development, data pipelines, APIs, infrastructure, evaluation, fairness, reliability, documentation, and institutional handover. The role is hands-on, requiring weekly coding, rapid prototyping, client collaboration, technical scoping for business development, reusable asset development, and mentorship of engineers and interns.

Design and develop scalable full-stack features using Java, Spring Boot, Python, FastAPI, Angular, and Azure. Lead technical decisions, backend architecture, API design, code reviews, engineering standards, deployment strategies, CI/CD improvements, cloud deployments, and observability. Collaborate with product, data science, and design teams to build AI-powered user experiences, contribute technical insights to roadmap planning, and balance hands-on coding with technical leadership.

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.

Design, develop, deploy, and operate secure full-stack applications with AI, machine learning, and large language models embedded as core capabilities. Build front ends, APIs, backend services, and data pipelines primarily in Python. Deliver cloud-native solutions across AWS, Azure, and hybrid environments while implementing security, scalability, testing, CI/CD, monitoring, and observability. Collaborate with business, technology, security, and operations teams to deliver enterprise-ready applications.

Build and productionize LLM-powered agents, workflows, and RAG systems. Apply prompt engineering, embeddings, fine-tuning, and traditional ML to automate tasks and improve products. Mentor teammates and help design scalable, production-grade AI infrastructure while staying current on LLM advances.

Designs and develops content governance platforms, anti-abuse tools, rule engines, and scalable governance pipelines. Integrates recommendation systems, implements content takedown policies, and builds capabilities for content review, risk detection, policy enforcement, and effectiveness analysis. The role addresses spam, fraud, traffic diversion, low-quality content, and black- or gray-market activity while collaborating with product, operations, algorithm, and business teams.

Leads analysis, mining, modeling, and visualization of large-scale transactional, operational, and customer data. Partners with compliance teams to prioritize analytical use cases, support regulatory requirements, improve business efficiency, and translate complex findings into actionable recommendations. Oversees multidisciplinary analysts, manages stakeholder data requests, assesses business changes, and develops analytical systems using machine learning, big data technologies, and visualization tools.

Lead the architecture and development of scalable AI infrastructure, distributed microservices, real-time data pipelines, and agentic AI systems for intelligent networking. Drive the full software lifecycle from design through production operations, establish technical direction, mentor engineers, and improve system reliability, security, observability, performance, and scalability.

Lead the technical vision, architecture, development, testing, deployment, and operations of scalable AI infrastructure, distributed microservices, real-time data platforms, and agentic AI systems. Build secure, resilient, observable services processing high-volume network data; guide technical strategy, improve engineering practices, and mentor engineers. Translate complex technical challenges into production-ready solutions across intelligent networking, cloud platforms, analytics, and automation.

Lead the architecture, development, testing, deployment, and operation of scalable AI infrastructure, distributed microservices, real-time data platforms, and agentic AI systems. Define technical vision, build secure and observable high-performance services, process large-scale network data, and integrate intelligent networking capabilities. Mentor engineers, lead technical discussions and reviews, improve system reliability and performance, and translate complex technical challenges into production-ready solutions.