Top Artificial Intelligence Jobs in Delhi

The AI Lead will design, develop, and implement AI solutions to enhance the SDLC stages, mentor teams, and ensure responsible AI practices.

Design, build, and deploy production-grade LLM, RAG, and agentic AI systems. Implement document intelligence pipelines, use LangChain/LangGraph/LlamaCloud, manage embeddings and vector DBs, and scale services on AWS/GCP/Azure with strong ownership.

Lead product strategy and architecture for Conversation AI, owning agent creation, deployment, evaluation, observability, integrations, pricing, and compliance. Partner with Engineering, AI, Design, GTM and operations to deliver dependable AI agents at scale, mentor PMs, and define metrics, testing, and release standards to ensure reliable business outcomes across millions of interactions.

Lead product strategy and platform architecture for HighLevel Voice AI. Define lifecycle, evaluation, quality standards, integrations (telephony, CRM, workflows), metrics, reliability, and commercial models. Partner cross-functionally with engineering, design, legal, GTM, and operations to ship dependable, scalable voice agents that handle inbound/outbound calls, transfers, actions, and observability across SMB and agency customers.

Build intuitive, high-performance React interfaces for AI-powered insurance workflows. Collaborate with design and backend teams, create reusable UI components and design systems, integrate frontend features with AI services (document review, entity extraction, quote comparison), rapidly prototype, and iterate based on user feedback.

Build and productionize AI features across frontend and backend: document review, entity extraction, quote comparison, embeddings and LLM integrations, prototype from customer feedback, and create scalable reusable systems for insurance workflows.

Lead WAIG's product function end-to-end for AI solutions in health, education, and agriculture. Define multi-year product vision and roadmap, ensure disciplined delivery from MVP to scale, build and mentor distributed product teams, engage governments and donors, and represent product capability in partnerships and fundraising.

Lead product strategy for HighLevel Knowledge Bases and Ask AI, defining architecture, ingestion, retrieval, evaluation, permissions, observability, and commercial models. Drive cross-team platform standards, onboarding, testing datasets, and integrations so AI assistants reliably retrieve context, invoke tools/agents, execute actions, and surface auditable, safe results for multi-tenant SMB customers.

Design, develop, fine-tune, and productionize ML and Generative AI/LLM solutions. Build end-to-end ML pipelines (data ingestion, modeling, deployment, monitoring), implement RAG and vector DB architectures, apply prompt/context engineering and MLOps best practices, and collaborate with cross-functional teams to optimize models for performance, latency, scalability, and cost.

The Content Writer/Ai Editor will create high-quality content while leveraging AI tools for optimization, managing multiple projects, and adhering to brand guidelines.

Design and implement AI-enabled ServiceNow solutions across ITSM, FSM, and ITAM. Provide technical leadership and ensure best practices in AI capabilities, facilitate stakeholder workshops, and guide project teams.

The Solutions Engineer will partner with customers to help them derive business value from OpenAI's products, manage pre-sales processes, and provide technical expertise.

The Senior Engineer will develop and deploy machine learning models and workflows, mentor junior engineers, and collaborate with cross-functional teams to improve patient care through innovative solutions.

Design, execute, and maintain test plans for an AI-powered platform. Perform functional, integration, regression, end-to-end, API, browser/device compatibility, and performance testing. Validate AI features, prompts, and generated responses; track and verify bug fixes; collaborate with developers and product teams; prepare launch-readiness reports and recommend stability and UX improvements.

Design and build production AI systems for BJAK's AI Neobank: AI agents, LLM apps, RAG/retrieval, tool-calling, and automations for onboarding, KYC, risk, CRM and operations, with focus on accuracy, latency, cost, security, compliance and reliability.

The AI Engineer will develop and deploy AI/ML models and solutions, collaborate with teams, and ensure production readiness while addressing complex business problems.

Design, implement, and lead large-scale generative AI and ML systems (LLM fine-tuning, RAG, agents, Graph ML). Build REST APIs, deploy with Docker/Kubernetes, optimize big-data workflows, mentor engineers, and operate ML services on cloud platforms.

Lead design and delivery of cloud-native, event-driven platforms and applications. Drive full lifecycle development, champion engineering best practices, implement production-ready solutions, and build AI/LLM capabilities like RAG, embeddings, and model CI/CD across multi-cloud environments.

Execute fashion design briefs using Raspberry AI to produce flats, tech packs, colorways, prints, mood boards, and concept visuals. Translate creative direction into precise prompts, iterate on feedback, maintain brand-consistent quality, document workflows, flag platform issues, and partner with US Customer Success for intake and delivery under tight turnarounds.

Build and ship AI-driven GTM systems: outbound lead engine, inbound/lifecycle automations, AI-assisted content pipeline, data/reporting layer, and fast production patches to eliminate manual marketing workflows.

As a Senior Data Scientist, you will utilize analytical methods and programming skills to derive insights from data, design solutions, and communicate effectively with stakeholders.

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.

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.

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.

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.