Top Artificial Intelligence Jobs in Delhi

Design, develop, deploy, and optimize production-grade Generative AI and LLM-powered systems. Build agentic pipelines with orchestration, tool integration, feedback loops, asynchronous processing, and event-driven architectures. Collaborate across operations, marketing, customer experience, and product teams to identify AI opportunities. Troubleshoot systems, maintain documentation, communicate technical findings, and deliver scalable solutions using Python, cloud platforms, modern AI frameworks, and containerization.

Lead research and engineering work on LLM-based architectures for dialog, search, and destination entry. Optimize LLMs via prompting and fine-tuning, integrate them with ASR, TTS, and vision systems, and collaborate cross-functionally to deliver performant, resource-constrained solutions for automotive voice assistants.

Annotate first-person industrial and commercial videos by segmenting actions, assigning timestamps and labels, and describing hands, tools, objects, and workflows. Review and correct model-generated annotations, ensure complete and consistent coverage, resolve unclear cases, and meet quality and productivity targets. Prior hands-on experience with Physical AI, robotics, or industrial egocentric video annotation is mandatory.

Lead the design and production deployment of LLM-powered AI agents, generative AI systems, and retrieval-augmented generation infrastructure. Build and fine-tune models, develop multi-step agents, apply data science methods, and establish evaluation, monitoring, observability, and feedback systems. Partner with product, backend, and infrastructure teams to scale AI capabilities across messaging, scheduling, sales, and operations while influencing the AI roadmap and mentoring engineers.

Lead architecture for enterprise AI initiatives across products, platforms, and shared capabilities. Define AI architecture standards, reference patterns, governance, security, and operational guardrails. Guide solutions involving LLM applications, retrieval systems, agentic workflows, orchestration, evaluation, observability, and cloud-native platforms. Partner with business, product, and engineering leaders, influence strategic decisions, mentor technical teams, and support AI-enabled software development and platform modernization.

Owns the full sales cycle for complex, high-value AI research workspace opportunities, from prospecting and discovery through negotiation and closure. Builds executive stakeholder relationships, generates new business, supports strategic renewals, leads workshops, develops business cases, and communicates measurable value from AI-powered research and knowledge workflow solutions. Requires enterprise sales expertise, AI fluency, consultative selling skills, and extensive regional travel.

Leads the DPI and Digital Governance consulting domain and the ICDIA programme. Owns offerings, quality, revenue pipeline, client and funder relationships, milestone-based delivery, budgets, risk management, team leadership, ecosystem engagement, and handover planning. The role requires extensive digital government or development consulting experience, multi-country programme leadership, donor-funded delivery expertise, knowledge of India’s DPI ecosystem, technical fluency, and willingness to travel 30 percent.

Build practical AI research systems for laboratory and customer use cases. Responsibilities include creating reinforcement learning environments, agentic systems, LLM pipelines, evaluation harnesses, benchmarks, and model-testing workflows. Run fine-tuning and model experiments, deploy local or self-hosted models, document reproducible results, and collaborate with GenAI researchers and cross-functional stakeholders to turn research concepts into reusable applications.

Lead the people and talent function for a rapidly scaling AI access organization in India. Build founding culture, talent strategy, structured recruiting and assessment processes, onboarding, performance management, HR systems, compliance, and data-driven people practices. Advise leaders on organizational design and sensitive personnel matters while coordinating with global teams and external HR partners.

Own the backend and agent systems powering LLM products, including multi-turn conversations, tool calling, RAG, state management, evaluations, monitoring, and production reliability. Diagnose failures across models, knowledge systems, tools, and infrastructure; establish regression testing and quality measurement; and guide product and technical decisions. The role requires shipping and improving LLM features at scale, strong backend engineering, disciplined evaluation practices, and preferably Web3 experience.

Own the end-to-end product experience for agentic AI products, partnering closely with the CPO, design, and engineering. Responsibilities include defining the roadmap, features, UX improvements, onboarding, in-app education, and product marketing content. The role requires deep product exploration, data-driven prioritization, identifying friction and gaps, creating comprehensive PRDs and prototypes, and shipping simple, high-impact improvements in a fast-moving startup environment.

Design, develop, and optimize scalable data platforms, pipelines, and processing workflows supporting analytics, business intelligence, and AI applications. Build solutions with Databricks and cloud technologies, integrate structured and unstructured data, implement quality and governance practices, optimize architectures, and support production deployment and monitoring. Collaborate with cross-functional teams to translate business requirements into reliable technical solutions and troubleshoot complex data challenges.

Implement enterprise ML and GenAI solutions in production, including AI agents, LLM automation, RAG, knowledge grounding, and industrialized processing pipelines. Establish ML lifecycle, MLOps, and LLMOps best practices; integrate Microsoft AI and Power Platform services; support deployments across environments; gather requirements; estimate work; present solutions; and document technical implementations.

Implement machine learning models in production and design enterprise GenAI, LLM, and AI automation solutions. Build and manage industrialized processing pipelines, establish ML lifecycle and MLOps/LLMOps best practices, integrate Microsoft AI and Power Platform services, and support data science teams. The role also involves gathering requirements, estimating work, presenting technical solutions, troubleshooting AI agents, and creating documentation.

Implement machine learning models in production and design enterprise GenAI solutions, AI agents, and scalable LLM/ML automation. Build and manage industrialized processing pipelines, MLOps and LLMOps frameworks, and model lifecycle best practices. Integrate Microsoft Copilot Studio and Azure AI services with Power Platform tools, implement RAG and knowledge grounding, support multilingual retrieval, and troubleshoot agents. Gather requirements, estimate work, present technical solutions, and create documentation.

Build and integrate AI agents, APIs, webhooks, databases, and business-system automations. Translate business processes and customer flows into simple, efficient workflows with minimal code. This is a remote, part-time individual-contributor consulting engagement requiring hands-on system building and immediate availability.

Designs and develops scalable cloud-based and serverless software across the company’s platform. Builds prototypes and production services, applies AI throughout the SDLC, promotes modern Agile and Well-Architected practices, troubleshoots customer and internal issues, develops comprehensive test suites, and coaches software engineers. The role requires extensive distributed application experience and backend expertise across multiple programming languages, databases, cloud technologies, integrations, and workflow orchestration.

Evaluate datasets and AI-generated outputs for accuracy, relevance, consistency, and quality. Provide written feedback to improve AI system performance, follow project guidelines, support quality assurance, and communicate findings clearly. The role requires strong critical thinking, analytical reasoning, attention to detail, organization, and independent work in a remote environment. Prior AI experience is not required; data annotation, content evaluation, quality assurance, research, and AI project experience are preferred.

Join Welo Data’s Hungarian contributor network for flexible, remote AI data projects. Depending on project needs, contributors may annotate text, images, or audio; evaluate search results, advertisements, or chatbot responses; create and test prompts for large language models; and assess cultural and linguistic accuracy. Projects vary from several days to weeks, with scope, timelines, and compensation provided before participation.

Deploys at utility customer sites to drive product adoption, observe platform usage, document UI, functionality, and analytical issues, and manage feedback with engineering. Builds relationships across customer organizations, presents demos to senior utility leaders and IAS officers, validates platform outputs against customer data, closes the loop on shipped features, and identifies expansion opportunities. Requires strong writing, technical curiosity, ownership, and comfort using AI tools.

Leads enterprise Microsoft 365 transformation initiatives focused on SharePoint Online, Power Platform, Copilot, AI agents, automation, security, compliance, and governance. Architects and implements collaboration solutions, migrates legacy platforms, configures Purview and related controls, and develops AI governance strategies. Provides technical leadership, client engagement, workshops, training, and ongoing support while coordinating project risks and dependencies. Requires availability during Eastern Time business hours and deep Microsoft 365, security, programming, database, and cross-tenant migration expertise.

Build and own end-to-end evaluation frameworks for LLMs and multi-step AI agents, including benchmarks, automated and human-in-the-loop evaluators, golden datasets, regression suites, production monitoring, and quality dashboards. Partner with research, product, Trust & Safety, and legal teams to define rigorous metrics and translate evaluation results into training and deployment decisions. Mentor engineers and establish evaluation best practices.

Design, prototype, deploy, and maintain AI-powered internal tools and automation for business teams. Build full-stack applications, agents, workflows, enterprise integrations, and reusable components using generative AI and cloud technologies. Ensure security, governance, testing, observability, documentation, and measurable business impact while supporting adoption across Sales, Support, Product, Operations, and other teams.

Own analytics and measurement for HighLevel’s AI product portfolio. Define instrumentation standards, feature-level KPIs, adoption and quality metrics, and rigorous approaches for evaluating nondeterministic AI features. Partner with experimentation leaders, PMs, engineering, and Analytics Engineering to close data gaps, influence product roadmaps, establish canonical metrics, and mentor future analysts.

Supports end-to-end planning and execution of AI and digital transformation programs for utilities and power-sector organizations. Responsibilities include assessing AI readiness, facilitating data access, coordinating pilots, engaging stakeholders, delivering capacity-building workshops, tracking KPIs and risks, producing reports and knowledge products, and supporting innovation frameworks, partnerships, proposals, and growth initiatives.