Top Artificial Intelligence Jobs in Delhi

Design and build production LLM-based agent systems, governance knowledge pipelines, hybrid retrieval and evaluation frameworks, and platform infrastructure. Implement behavioral shaping, observability, and constraints to ensure agents align with governance and compliance requirements, collaborating with product and data teams.

Drive the expansion of software development and AI services by managing client relationships, building sales pipelines, and negotiating contracts.

Provide hands-on technical support to customers deploying, operating, and scaling ArangoDB-based AI systems. Diagnose issues, optimize queries and performance, configure deployments across Kubernetes and cloud/on-prem, debug networking/storage, script automations, and document solutions while collaborating with Product and Engineering.

Lead architecture and solutioning for enterprise-grade, AI-enabled, cloud-native applications. Collaborate with clients, create feasibility assessments and POCs, ensure scalable, secure platforms, and mentor delivery teams while representing the company in client meetings.

Build and maintain merchant-facing payment products (onboarding, dashboards, checkout, transaction history). Develop payment APIs, integrate with banks/payment partners, design DB schemas for high-throughput transactions, use AI coding tools to accelerate development while validating AI-generated code, ship features end-to-end, and maintain production reliability.

Lead development of agentic workflows and LLM systems, build LLM evaluation/tracing and prompt operations pipelines, integrate MCP-based systems, deploy and scale enterprise-grade AI/ML solutions, and collaborate with product and engineering teams.

The role involves building and integrating LLM-powered systems, requiring expertise in Python, agent frameworks, and modern API development.

Create, review, and refine advanced mathematical problems, solutions, and proofs for AI training datasets. Design challenging problems, validate reasoning, identify errors, and produce clear technical explanations. Apply research methodology, provide structured feedback, and collaborate with project teams to improve mathematical content quality.

Design and presales of end-to-end AI/HPC infrastructure: size GPU clusters, storage, networking, Kubernetes/container platforms, integrate liquid-cooled racks with facility constraints, validate solutions with OEMs, and support delivery for scalable, operable AI deployments.

Lead AI advisory and policy support for government and social-sector clients in India: design and deploy AI-native systems and governance frameworks, originate and manage advisory and evaluation engagements, build client relationships, direct technical teams, manage budgets and practice growth, deliver thought leadership, and design trainings to strengthen AI transformation in public-sector settings.

Design, develop, and deploy ML models and pipelines; preprocess data and engineer features; implement MLOps (Docker, Kubernetes, CI/CD); collaborate with stakeholders to translate business problems into AI solutions; monitor and optimize model performance and maintain documentation and governance.

Own post-sale success for enterprise customers: drive adoption, retention, and expansion of Cursor. Lead end-to-end rollouts, engage engineering leaders, convert use cases to repeatable workflows, monitor usage, and relay product feedback to influence roadmap.

Design and build agentic AI solutions and complex agent workflows using Python. Train and deploy models on AWS or other cloud, build prototypes with Streamlit/Gradio, apply MLOps, collaborate in Agile/Scrum with Git and JIRA.

Lead design, operation, and reliability of large-scale AI/HPC clusters. Manage day-to-day operations, incident response, automation, performance tuning, scheduler and storage optimization, and collaborate with researchers and global teams to improve GPU-accelerated infrastructure.

The AI Platform Engineer leads the development of production AI agents and components, collaborates on AI implementations, and maintains AI systems for reliability and observability.

Build and maintain Python/FastAPI backend services and React frontends, design and consume REST APIs, work with relational databases, and implement/debug AI-enabled features including vector DBs, embeddings, semantic search, and RAG workflows. Improve existing codebases, collaborate with product and design, participate in testing, deployment, and monitoring, and ensure performance, security, and maintainability.

The IT Operations Engineer will manage AI and Data platforms, ensure operational health, lead incident resolution, and automate tasks while collaborating with engineering teams.

Lead end-to-end enterprise sales for AI Services in India: define ICP and GTM, close complex deals, build pricing and sales motions, coach and scale a high-performance sales team, and partner with Product/Tech/Ops to iterate offerings.

Lead generative AI image production for realistic personas and models. Build and optimize scalable generative pipelines, automate batch workflows, evaluate new models/tools, define technical direction and best practices, and improve image quality, consistency, speed, and cost-efficiency.

Lead architecture and engineering of enterprise-scale Generative AI and data platforms. Design reusable platform services for end-to-end AI/ML lifecycle, build agentic applications and event-driven systems, enforce CI/CD and DevSecOps, ensure security/governance/observability, mentor engineers, and drive platform modernization using cloud-native, containerized, and distributed technologies.

Lead platform automation for enterprise Generative AI and data platforms: design and build IaC, CI/CD, observability, deployment automation, and self-service capabilities to accelerate AI/ML lifecycle, reliability, governance, and developer productivity across cloud-native distributed environments.

Lead end-to-end design, build, deployment and iteration of AI-agent solutions. Embed with clients to map workflows, architect multi-agent systems, integrate enterprise systems and RAG pipelines, implement guardrails and observability, and liaise with non-technical stakeholders to drive adoption.

Build and maintain lab environments and CI/CD pipelines for the Skylar AI platform. Create automated end-to-end tests, simulate user journeys and telemetry, validate integrations and performance, manage versioning and rollback tests, support data generation, and document environments. Partner with product, data science, QA, and engineering to ensure reliable monthly updates and platform availability.

The ServiceNow Solution Architect - AI role involves leading and mentoring a team, designing and deploying AI-driven applications, managing application development, and integrating solutions into ServiceNow modules. Responsibilities include coordinating with stakeholders, troubleshooting, and ensuring adherence to technical standards.

Design, build, test, and ship production AI-enabled software and reusable components for enterprise workflows. Implement context-aware agentic systems, integrate models and APIs, write unit tests, debug production issues, lead code reviews, mentor engineers, participate in on-call rotation, and collaborate with U.S. business partners while working remotely from India.