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.

Sell and manage solution-level offerings in industrial automation, digital twin, AI and cybersecurity. Build customer relationships, promote Yokogawa technologies across industries, and support sustainable, technical solution adoption.

Creates and scales AI-enabled digital learning content for customers, partners, and internal teams. Produces product videos, tutorials, interactive modules, onboarding journeys, certifications, assessments, and enablement assets. Uses AI and multimedia tools for scripting, editing, translation, voiceover, graphics, and content repurposing. Partners with subject matter experts to simplify technical concepts, manages Academy publishing and content quality, and uses engagement analytics to improve learning experiences and self-service adoption.

Build enterprise AI agents using Microsoft Copilot Studio and Azure AI Foundry. Develop topics, triggers, routing, fallback handling, generative answers, RAG, knowledge grounding, citations, metadata-based retrieval, multilingual capabilities, and prompts. Integrate agents with Power Automate, SharePoint, Dataverse, Teams, Azure AI Search, and Azure OpenAI or Foundry models. Support troubleshooting, security, environments, ALM, and DEV/UAT/PROD deployments.

Build and deploy production AI systems across LLM orchestration, retrieval, agents, evaluations, voice, APIs, backend services, and infrastructure. Design reliable LLM pipelines, evaluation suites, data flows, and real-time multimodal experiences. Experiment with emerging models, debug issues across the stack, and communicate technical decisions clearly while owning projects from exploration through production iteration.

Work directly with pharmaceutical customers to understand workflows and design, prototype, integrate, and deploy AI-powered solutions. Translate ambiguous requirements into technical architectures, build Python-based integrations and data workflows, lead technical discovery and implementation workshops, debug APIs and cloud deployments, and collaborate with customer and internal engineering teams. Bring recurring customer needs to product and engineering while supporting regulated enterprise AI deployments.

Build production-grade AI applications for pharmaceutical commercial, medical, and patient workflows using LLMs, retrieval, agents, structured data, and multimodal AI. Develop scientific knowledge systems, evaluation frameworks, and reliable, safe AI solutions using healthcare and life sciences datasets. Collaborate with pharma customers and domain experts to translate complex workflows into scalable technical products.

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.

Develop scalable enterprise applications and REST APIs using Python or .NET/C#, integrate Azure and Microsoft Fabric services, build SQL-based data and ETL/ELT solutions, and support Lakehouse, OneLake, and Data Warehouse architectures. Integrate LLMs and RAG capabilities into enterprise applications, troubleshoot application and data-pipeline issues, and collaborate with product, data, AI, and business teams. Backend and integration development are the primary focus, with working knowledge of modern front-end frameworks expected.

Designs, operates, and improves scalable cloud infrastructure across GCP and AWS. Builds Terraform-based infrastructure, GitHub Actions CI/CD pipelines, observability, automation, and AI-enabled monitoring and remediation. Manages MySQL and SQL Server databases, Cloudflare services, security controls, repository governance, and incident response. Supports reliability, performance, cost optimization, compliance, and cloud modernization while providing 24/7 on-call coverage and mentoring junior engineers.

Architect and build production-grade generative AI and agentic AI applications, including LLM workflows, multi-agent systems, RAG pipelines, APIs, and ML/GenAI infrastructure. Develop and optimize models, manage deployment and monitoring, apply responsible AI practices, and collaborate directly with customers. The role also involves mentoring engineers and shaping AI platform strategy.

Build and deploy production-ready AI agents and multi-agent systems on Azure. Design RAG pipelines, orchestration patterns, tool integrations, APIs, prompt engineering workflows, evaluation loops, and unstructured-data pipelines. Implement CI/CD, monitoring, responsible AI controls, and enterprise integrations with Microsoft Teams and M365 Copilot. The role requires hands-on GenAI/LLM engineering, Python development, cloud infrastructure expertise, and flexibility across data engineering, low-code configuration, and AI-native development tools.

Design, develop, and optimize scalable data platforms, pipelines, and processing workflows using Databricks and cloud technologies. Integrate structured and unstructured data, develop models, implement quality and governance practices, and support analytics, business intelligence, machine learning, and AI use cases. Collaborate with cross-functional teams to translate requirements into technical solutions, troubleshoot production issues, modernize architectures, and improve reliability, performance, and cost.

Own a workstream on a national rural digital platform, from defining use cases and functional specifications through engineering delivery, field testing, rollout, training, and adoption tracking. Collaborate with government counterparts, self-help groups, community workers, and block staff to gather requirements and resolve issues. Prepare acceptance criteria, test delivered functionality, report adoption honestly, and provide actionable updates to senior leadership. One workstream focuses on member-facing applications; the other covers financial systems, bookkeeping, and credit linkage.

Own data governance and consent for a national rural platform serving millions of self-help group members. Responsibilities include mapping data and purposes, designing multilingual consent flows, creating access, retention, deletion, sharing, and lineage frameworks, translating Indian data protection law into engineering requirements, testing flows with communities, supporting government handover, and preparing actionable briefs. The role requires field engagement, policy and technical fluency, Hindi, and 25–30% travel.

Design, build, test, and deploy production-grade agentic AI systems using Python, LangGraph, LangChain, AWS Bedrock, and orchestration tools. Integrate AI agents with APIs and business systems, implement memory and retrieval, establish observability and safeguards, and develop reusable SDKs and templates. Collaborate directly with clients to define requirements, run experiments, and rapidly deliver reliable AI solutions using modern software engineering, cloud deployment, and CI/CD practices.

Designs and maintains a centralized AI platform and agentic framework for secure enterprise deployment and access. Builds scalable AI agents, unified APIs, distributed systems, and microservices; integrates agents into customer and internal applications; optimizes cloud-native workloads; ensures security and regulatory compliance; and leads technical strategy, mentoring, performance analysis, and capacity planning.

Build governed Databricks Lakehouse foundations and productionize machine learning and GenAI solutions, including forecasting, anomaly detection, NLP, RAG, and predictive analytics. Develop reliable batch and streaming workflows, CI/CD, monitoring, governance, security, lineage, and semantic models. Collaborate with product, data, platform, and domain teams to deliver measurable customer value while meeting reliability, compliance, cost, and operational objectives.

The Business Ecosystems Manager will support partnerships for industrial adoption of emerging technologies while driving digital transformation and managing stakeholder relationships.

Lead discovery, rapid prototyping, and deployment of production-ready AI solutions for supply chain and manufacturing. Build Python-based RAG pipelines, semantic search, multi-agent workflows, data integrations, and enterprise platform connections. Engage frontline operations, translate business needs into technical requirements, manage agile delivery, and ensure prototypes are scalable and transferable to core engineering teams. Work across SAP, MES, shop-floor platforms, databases, telemetry, and unstructured data while collaborating with security, data, and architecture teams.

Lead AI-driven digital marketing: create SEO-optimised content, run social and paid campaigns, perform SEO and technical audits, implement Zoho One automation, analyse performance with GA4 and Search Console to generate quality B2B leads.

Coordinates the AI Accountability Network’s newsletters, training programs, grants, fellowships, payments, events, communications, and community initiatives. Manages editorial workflows, contractors, applications, logistics, websites, listservs, dashboards, metrics, and stakeholder communications. Supports grant administration, funder reporting, travel, curriculum development, and website publishing while improving processes and tracking program impact.

Build and refine AI-powered prototypes for supply chain and manufacturing operations. Engage with factory and warehouse teams, translate operational needs into software requirements, develop Python-based RAG and agentic systems, integrate APIs and data sources, and iterate prototypes based on frontline feedback. The role involves SQL and document data preparation, agile collaboration, occasional manufacturing-site travel, and progressive adoption of enterprise AI frameworks and production engineering practices.

Design, build, and scale production AI agents across EveryWatch. Own the agent roadmap, architecture, shared tools, memory, state, observability, evaluation, and cost controls. Harden WatchChat, develop RAG and tool-calling systems, establish measurable quality standards, and ship solutions directly in Python. Collaborate with sales, product, data, and engineering teams to identify high-impact use cases and take agent concepts from discovery through production.