Top Artificial Intelligence Jobs in Delhi

Evaluate and author high-quality computer science content to improve AI model reasoning and accuracy. Analyze complex CS problems, create scenarios/datasets, review AI outputs for correctness, and collaborate remotely to refine guidelines and documentation.

Support and enhance ADI's global compliance program aligned to enterprise risk and ISO 37301. Monitor regulatory developments, update policies, execute risk‑based monitoring and testing, manage investigations and remediation, support audits, develop training and communications, track metrics, and collaborate with Legal, HR, Finance, IT, and Operations to drive continuous improvement.

Lead and support execution of ADI's global compliance programs: risk‑based monitoring, policy management, testing, investigations, training, ISO 37301 alignment, audits, remediation, and cross‑functional stakeholder coordination.

Quota-carrying enterprise sales role focused on generating, qualifying, and closing AI opportunities across an assigned region. Partner with field teams and AI ecosystem partners, craft executive-quality pitch decks and proposals, manage CRM and forecasts, and translate AI technologies into business value. Drive both infrastructure/product sales and consulting engagements through the full sales cycle while embedding into account planning and co-selling motions.

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.

Designs and implements computer vision and machine learning algorithms for aquaculture applications. Responsibilities include developing prototypes, conducting experiments, building data-processing and training pipelines, maintaining model testing and statistical verification, extending AI codebases, documenting research, leading long-term research, recruiting talent, and tracking AI developments. The role may also involve production deployment, inference optimization, MLOps, cloud platforms, and occasional visits to aquaculture sites.

Drive process improvement, automation, and analytics across SOX, Internal Audit, and Enterprise Risk Management functions. Map workflows, eliminate manual effort, build CI/CD-enabled automation and testing frameworks, and develop Power BI dashboards using Python and SQL. Configure Jira workflows and compliance automation, implement compliance-as-code monitoring, and apply GenAI with audit-ready guardrails. Partner with technology and compliance teams to deliver scalable, measurable, and defensible solutions.

Design, develop, and optimize autonomous AI agents capable of reasoning, planning, tool use, and multi-step execution. Build agent frameworks, human-AI collaboration mechanisms, monitoring and evaluation systems, and production integrations. Apply large language models, prompt engineering, reinforcement learning, planning algorithms, workflow orchestration, and system design. Collaborate cross-functionally while researching advances in agent technology and improving reliability, accuracy, safety, and efficiency.

Support product operations by collecting and tracking user feedback, coordinating product issues across teams, organizing requirements, analyzing basic data, maintaining web pages, and assisting with AI-related projects. The role requires cross-functional communication, problem-solving, execution skills, bilingual English and Mandarin proficiency, and availability for 3–5 days per week for at least six months.

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.

This job is for candidates with AI infrastructure experience to submit their resumes for future opportunities. No specific role is posted.

As a Senior Agentic AI Lead, you will manage the agentic AI domain, engage with customers, oversee the AI lifecycle, and ensure effective data governance and analytics across various teams.

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.

Build and scale a full-stack Next.js application tracking PRs, CI health, test parity, and benchmarks across AI accelerator open-source projects. Integrate GitHub APIs and webhooks, design PR/metrics schemas, visualize results, and produce technical digests and reports on cross-accelerator parity and performance trends.

The Vendor Program Manager develops vendor marketing plans, manages product categories, coaches staff, and reports on marketing performance while ensuring profitability and alignment with vendor objectives.

Build production-grade agentic AI systems that reason, search, and execute workflows. Develop RAG pipelines, AI agents, semantic search layers, data processing solutions, dimensional data models, and scalable AI workloads. Work with structured and unstructured data, LLMs, vector databases, cloud infrastructure, CI/CD, and ML observability. Responsibilities include fine-tuning models, prompt engineering, optimizing context windows, and deploying reliable AI applications.

Develop and deploy AI/ML models using Python. Focus on model development, production deployment, and collaborating with cross-functional teams. Communicate findings, solve technical problems, and work in a fast-paced environment. React and TypeScript experience and AI security/risk familiarity are nice-to-haves.

Lead enterprise AI strategy and productization for research transformation: scope, develop, test, and scale AI tools/agents, advise clients, vet third-party tools, create documentation and upskilling materials, and present outcomes to senior leadership.

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.

Serve as the technical pre-sales architect for regional AI PODs: design end-to-end AI architectures (GPU clusters, networking, storage, MLOps, inference), validate solutions with NVIDIA and cloud partners, lead technical discovery and proofs-of-concept through the ATC, and ensure clean handoff to delivery while driving deal momentum.

Evaluate AI-generated technical content, craft and answer advanced ML/AI prompts, assess model responses for accuracy, relevance, and clarity, identify errors or hallucinations, and provide feedback to improve AI systems.