Top Artificial Intelligence Jobs in Delhi

The role involves reviewing AI-generated outputs, generating content, and ensuring cultural accuracy in various media formats. Attention to detail and analytical skills are essential.

The AI Product Manager will lead product discovery and strategy, prototype solutions using AI tools, perform competitive analysis, track product metrics, advocate for user experience, and collaborate with different teams.

Design, build and ship native Android features for an AI-enabled neobank using Kotlin. Integrate backend APIs, improve performance and reliability, collaborate with product and design to simplify financial user journeys, and apply on-device ML where it improves UX.

Build and ship native Android features in Kotlin and Jetpack Compose; integrate REST APIs; improve performance, crashes and memory; collaborate with product/design; use analytics and on-device AI (TensorFlow Lite) to enhance user journeys for insurance and financial services.

The Applied AI Researcher will design experiments, train models, and build pipelines for a GenAI platform while contributing to research and deploying systems in production.

Responsible for leading end-to-end AI research projects, developing domain-specific models, mentorship, and optimizing research-to-production processes while collaborating across disciplines.

The Principal Applied AI Researcher will define and lead domain-specific model strategies, manage model lifecycle, mentor researchers, and influence platform decisions in AI research.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

Assist in developing AI solutions for financial planning, working on projects involving data structures, algorithms, and integrating AI features into the platform.

The AI Manager will lead a global team in AI project delivery, manage AI roadmaps, oversee LLMOps pipelines, and engage with stakeholders while ensuring compliance and performance metrics are met.

The AI QA Specialist will design and execute test strategies for AI systems, validate model outputs, implement monitoring dashboards, and ensure compliance with safety standards.

The MDM Specialist will design and implement master data management solutions, integrate AI workflows, and ensure data governance and quality across customer and vendor domains.

Design and implement production-grade AI models and algorithms, lead AI initiatives across organizations, enhance Agentic AI capabilities, and collaborate with teams to develop high-impact AI features.

Leads the architecture, development, deployment, and operation of scalable AI infrastructure, distributed systems, real-time data platforms, microservices, and agentic AI solutions. Defines technical vision, builds resilient and observable production services, processes high-volume network data, mentors engineers, and drives engineering excellence across intelligent networking and automation platforms.

Build and deploy full-stack AI applications integrating LLMs, RAG architectures, vector databases, and multi-agent systems. Develop Python backends, APIs, ingestion pipelines, responsive interfaces, and real-time dashboards. Design and optimize PostgreSQL databases, containerize applications with Docker and Kubernetes, manage AWS and GPU infrastructure, and operate Linux environments. Mentor developers, conduct demonstrations, evaluate emerging AI technologies, and improve reliability through testing, debugging, performance tuning, and quality processes.

Leads the architecture and delivery of production-grade agentic and generative AI systems, including LLM agents, RAG pipelines, multi-agent workflows, and scalable cloud applications. Guides technical architecture, production engineering, LLMOps, evaluation, observability, security, and responsible AI practices. Mentors AI/ML and backend engineers, conducts design and code reviews, and collaborates with product, data, platform, security, compliance, and executive stakeholders.

Leads end-to-end cloud and platform engineering consulting engagements, owning scope, governance, delivery quality, client relationships, and executive outcomes. Manages a 2–3 person engineering pod through staffing, coaching, and performance development. Oversees multi-cloud discovery across AWS, Azure, and GCP, develops prioritized recommendations and roadmaps, applies AI-driven solutions, and evolves repeatable Rapid Solutions playbooks, templates, and delivery standards.

Lead end-to-end cloud and platform engineering consulting engagements, owning scope, governance, client relationships, delivery quality, and executive outcomes. Manage and coach a 2–3 person engineering pod, oversee staffing and utilization, conduct multi-cloud technical discovery, and develop prioritized recommendations and roadmaps. Evolve Rapid Solutions offerings, playbooks, templates, and delivery standards while applying AI-driven solutions to improve efficiency and insight.

The Partner AI Deployment Engineer will guide AWS engagements, shape technical strategy, build AI architectures, and mentor partners to drive successful AI deployments.

Build the AI layer for SigNoz: implement agent orchestration, long-running execution, streaming APIs (SSE), and MCP server integrations to expose observability data to LLM agents. Ship production LLM features, design scalable distributed backends in Go and async Python, and drive projects end-to-end in a remote-first environment.

Design, develop, and deploy generative AI agents and end-to-end ML pipelines. Lead prompt engineering, LLM integration, model training, and productionization, collaborating with customers and stakeholders to deliver AI solutions.

Own strategy, roadmap, and execution for HighLevel VoiceAI. Deliver reliable inbound/outbound AI voice agents integrated with telephony, CRM, workflows, and analytics. Improve agent setup, call quality, compliance, APIs/webhooks, monetization, and developer extensibility. Partner with engineering, design, support, and GTM to drive adoption, instrumentation, and measurable business outcomes for SMBs and agencies.

Lead full-stack development using Python and FastAPI; build high-performance APIs integrated with AWS (SQS, Lambda). Implement front-end using React/Angular, manage Mongo/MySQL/Oracle databases, integrate AI features via prompt engineering and GitHub Copilot/Cursor, optimize performance, apply DevOps/CI-CD, troubleshoot and collaborate in Agile/Scrum with Jira and Git.

Design and lead GenAI and agent-based AI solutions for large-scale modernization programs. Define prompt engineering, orchestration, validation, guardrails, and human-in-the-loop workflows. Apply AI to legacy systems (COBOL, HP NonStop, C++) for code comprehension, dependency mapping, documentation, and test generation. Collaborate with solution and platform architects, work in Agile sprint-based delivery, and guide distributed offshore/onshore teams.

Design, build, and deploy production-grade AI automation and agentic workflows for payments processes (reconciliations, audit and reporting). Integrate AI systems into operational pipelines to automate high-effort financial operations while preserving control, audibility, and reliability.