Top Artificial Intelligence Jobs in Delhi

Lead and align Engineering, ML, and Analytics to deliver scalable, reliable production AI and data systems. Own technical direction, architecture, MLOps, and platform reliability while partnering with Product, Sales, and Customers to ensure engineering work maps to measurable business outcomes.

Remote 14-day pilot requiring native/expert Indian English speakers to use a qualifying Google Pixel device as their primary phone, interact naturally with notification-generating apps, complete ~10–15 minutes of daily notification-sorting tasks and short feedback forms via Upwork, and report issues. Participants must own the device, have reliable internet, and be international freelancers (not U.S. residents).

Create high-impact motion content for product launches, campaigns, and marketing across social, ads, landing pages, and product demos. Translate features into engaging animated stories and lead motion across formats (reels, carousels, micro-interactions). Use AI-powered tools to accelerate production, collaborate with marketing, content, and product teams, and maintain a consistent motion language. Manage multiple projects while staying organized, fast, and detail-oriented.

Perform remote, part-time data tasks to improve AI content in Marathi, including data collection, annotation, pairwise comparisons, counting, and object tagging across audio, video, images, and collected data.

Design and implement AI-powered data pipelines and workflows combining LLMs, embeddings, vector search, and RAG to transform structured and unstructured data into business-ready outputs. Build APIs and full-stack integrations, produce Excel/PDF/Word deliverables, optimize relational and vector data stores, and prototype scalable AI solutions while contributing to engineering best practices.

3–6 month internship supporting risk operations: monitor KRIs and case trends, assist investigations and case analysis, escalate findings, and help optimize AI/LLM tools, automation, and alerting workflows.

Lead architecture and production of complex multi-agent LLM systems. Design evaluation pipelines (Evals), optimize RAG and vector search for scale, manage cost and latency tradeoffs, translate product requirements into agent behaviors, and mentor junior AI engineers.

Design and implement semi-autonomous AI agents and orchestration layers, integrate models with third-party tools, build feedback/learning systems, and maintain LLM pipelines and MLOps for enterprise environments.

Lead optimisation of cloud and enterprise AI investments by implementing FinOps practices, monitoring and reporting spend, creating cost-allocation and chargeback models, governing AI adoption, partnering with technical and business teams, providing forecasting and strategic recommendations, and driving measurable savings and accountability across the organisation.

The AI Engineer will develop and deploy GenAI applications, serve as a technical advisor, and collaborate on strategic initiatives in AI at Databricks.

Lead the design, development, deployment, and optimization of AI-driven applications, especially RAG-based chatbots and generative AI solutions. Build scalable systems with Python and Java, deploy them on AWS and Azure, and apply machine learning, deep learning, NLP, vector databases, embeddings, and LLM integrations. Collaborate with cross-functional teams, manage technical deliverables, mentor engineers, ensure quality through testing and optimization, and adopt emerging AI technologies.

Lead design and delivery of RAG-based chatbot and AI solutions: architect scalable Python/Java systems, deploy and manage on AWS/Azure, mentor engineers, ensure quality, and research emerging AI methods.

Lead AI R&D: design, develop, deploy scalable AI/ML and deep learning solutions for structured and unstructured data in BFSI product areas. Manage technical teams, mentor staff, integrate AI into products, ensure model scalability and production-readiness, and monitor AI trends and methodologies.

Architect, build, and operate end-to-end AI automation systems: CLI frameworks, MCP servers, agent tooling, production pipelines, evaluation and monitoring. Own prompt and context engineering, multi-agent orchestration, integration with external tools, governance, and metrics to measure automation and business impact.

Customer-facing AI Engineer who designs, builds, and deploys production-grade LLM and agent-based solutions. Responsibilities include architecting agent systems, implementing RAG pipelines, integrating with platform signals and vector DBs, overseeing end-to-end ML lifecycle, writing clean Python code, and collaborating with customers and internal teams to ensure scalable, monitored deployments.

The role involves supporting Singapore-based tech startups in tech solution consulting, conducting workshops, and collaborating with industry partners to drive growth and digital transformation.

The AI Data Specialist will perform data collection, evaluation, annotation, and labeling tasks for AI-generated content in Tamil.

Perform data collection, evaluation, and annotation to improve AI-generated Kannada content. Tasks include pairwise comparisons, counting, object tagging and labeling across audio, video, images, and other data types.

Perform remote part-time data tasks to improve AI-generated Telugu content: collect, evaluate, and annotate data; conduct pairwise comparisons, counting tasks, and object tagging/labeling across audio, video, images. Flexible schedule, 10+ hours/week.

The consultant helps in AI project scoping, requirement gathering, business process analysis, and ensures alignment of AI projects with strategic objectives and user needs.

Lead strategy, roadmap, and execution for Maropost's Da Vinci AI platform. Prioritize AI opportunities across product clouds, drive assistive/agentic automation and personalization, establish responsible AI governance, partner with cross-functional teams from discovery to launch, measure business impact, and present outcomes to executive leadership.

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.

Work across growth, product, user research, and market analysis using AI tools. Run end-to-end GTM experiments, own funnel metrics, test product releases, and fix urgent operational issues. Communicate findings clearly and move quickly to ship improvements.

Lead design and build of an end-to-end AI contract-review agent: prompt orchestration, RAG pipelines, fine-tuning, web UI and tooling, data/model infra, deployment (CI/CD/cloud), and product-driven features like assumption mapping and contract diagnostics.

Own the roadmap and delivery of AI automation products, including reliable AI agents, tool-calling workflows, n8n automation pipelines, API integrations, and React-based interfaces. Partner with sales, content, and marketing to identify opportunities, then monitor, debug, and improve shipped solutions. The role requires strong product ownership, hands-on AI and automation experience, technical range across frontend and integrations, and fluency with LLMs and agent frameworks.