Top Artificial Intelligence Jobs in Delhi

Build and productionize LLM-powered agents, workflows, and RAG systems. Apply prompt engineering, embeddings, fine-tuning, and traditional ML to automate tasks and improve products. Mentor teammates and help design scalable, production-grade AI infrastructure while staying current on LLM advances.

Leads enterprise technology strategy, delivery, and transformation across Finance, FinTech, Legal, and Risk. Oversees product platforms, Agile and DevOps adoption, AI and data initiatives, digital transformation, change management, vendor partnerships, governance, and technology operations. Partners with executive stakeholders to align global priorities, integrate platforms, manage dependencies, and deliver scalable, secure, value-driven outcomes across a federated enterprise technology organization.

Build and productionize machine learning, generative AI, LLM, and automation solutions. Design industrialized processing pipelines, implement MLOps and LLMOps best practices, develop enterprise AI agents using Microsoft Copilot Studio and Azure AI Foundry, and integrate Microsoft and Azure services. Gather requirements, estimate work, present technical solutions, troubleshoot agents, and create documentation.

Designs and governs enterprise AI, data, analytics, cloud, and platform architectures. The role develops AI reference architectures, data engineering standards, cloud infrastructure patterns, resilience controls, responsible AI frameworks, and technology roadmaps. It guides engineering teams, reviews strategic solutions, establishes governance, mentors technical staff, and partners with business, security, compliance, and senior leadership to enable scalable, secure, cost-effective AI and data platforms.

Build and deploy production AI solutions natively in Snowflake, including machine learning models, Cortex Agents, document intelligence, semantic data access, and search services. Develop evaluation frameworks, assess AI quality, work with messy business data, and communicate findings to stakeholders. Collaborate with platform engineers while owning problem framing, experimentation, solution development, governance, and production delivery.

Build and deploy production AI solutions natively in Snowflake, including machine learning models, Cortex Agents, document intelligence, semantic data access, and search services. Develop evaluation frameworks, assess model and LLM quality, work with messy business data, and communicate findings to stakeholders. The role covers the full AI project lifecycle from problem framing and prototyping through governance and production deployment.

As a Senior Business Systems Analyst, you will define system goals, capture business requirements, and work with engineering teams on SFDC implementations.

Conduct AI visibility audits across ChatGPT, Perplexity, and Google AI Mode; identify content, schema, and technical improvements; use AI tools to run scripts; prepare client reports and updates; and document repeatable processes. The role involves live client work, communication, experimentation, and learning SEO, analytics, and AI visibility tools on the job.

Lead enterprise architecture strategy for a global financial institution. Review fraud-domain application portfolios, assess architecture options and build-versus-buy decisions, conduct rationalization assessments, define target-state “north star” architectures, and create estimates, roadmaps, and transition architecture diagrams. Serve as a technical authority while translating complex business challenges into scalable solutions and collaborating with technical and business stakeholders.

Leads emerging product innovation from opportunity discovery through prototyping, pilots, product-market validation, commercialization, and scale. Owns AI, data, analytics, automation, and platform product strategies; establishes experimentation and stage-gate governance; develops investment cases and go-to-market plans; and aligns cross-functional teams and executives. Manages product leaders, builds incubation practices, and drives measurable customer, revenue, efficiency, and strategic outcomes.

Lead AI and machine learning engineering activities for customers, partners, and prospects. Advise on GenAI/ML solutions, architect end-to-end workflows from data ingestion through deployment, lead proofs of concept, support customer delivery, and manage and mentor a team of AI and machine learning engineers. The role also involves pre-sales support, consultative solution design, technical presentations, and stakeholder engagement.

Leads information security governance, cyber risk management, compliance, audit readiness, client assurance, and AI governance programs. Partners with cross-functional teams, oversees regulatory and industry framework alignment, manages risk assessments and remediation, and leads a team of Cyber GRC professionals. Advises customers, regulators, auditors, and executives on security posture while supporting responsible AI adoption and emerging risk management.

Leads end-to-end power, performance, and area optimization for AI semiconductor SoCs. Influences architecture, RTL, memory hierarchies, dataflows, floorplanning, multi-voltage domains, synthesis, placement, clock-tree synthesis, routing, and power-analysis workflows. Partners with architecture, RTL, physical design, EDA vendors, and software teams to develop custom methodologies and deliver energy-efficient silicon under stringent PPA constraints.

Edit and optimize AI-generated education and test-prep content for accuracy, readability, traditional SEO, AI citation readiness, and product compliance. Responsibilities include fact-checking authoritative sources, updating outdated information, improving metadata, structure, internal linking, and search intent alignment, auditing published articles, enforcing style standards, managing editorial workflows, and providing feedback to improve AI-generated drafts.

Design, build, deploy, and operate production AI/ML systems on Google Cloud. Develop clean, tested Python; evaluate model quality; manage cloud networking and security; and ensure data and models remain within client boundaries. Explain AI trade-offs to clients, collaborate with Google Cloud teams, and own outcomes from prototype through reliable production use.

Leads a 3–6-person AI-focused engineering team while contributing at least 50% directly to software development. Responsibilities include architecture, full-stack coding, code reviews, testing, documentation, mentoring, delivery, and collaboration with product, design, and QA teams. The role requires commercial LLM experience, strong C# and JavaScript development skills, knowledge of databases, REST APIs, microservices, cloud platforms, and software engineering management.

Join Welo Data’s Swahili contributor network for potential remote AI projects involving text, image, or audio annotation; search, advertising, or chatbot evaluation; prompt creation; and cultural and linguistic accuracy review. Applicants must be native or near-native Swahili speakers based in Kenya, Tanzania, or the Democratic Republic of Congo, with English proficiency, digital-tool comfort, and strong attention to detail. Projects vary in duration and compensation, with details provided before participation.

Own the data marketplace product lifecycle, improving vendor onboarding, submissions, quality feedback, offerings management, and buyer discovery. Partner with engineering, research, GTM, and operations to convert complex requirements into reliable workflows. Define marketplace health and adoption metrics, use qualitative and quantitative insights to prioritize product improvements, and guide launches and iteration in a fast-growing AI startup.

Develop AI-driven products through hands-on software development and product engineering. Build minimum viable products, solve problems creatively, and help shape future AI applications. The role is available remotely or in Boston and Chicago, with contract options ranging from two weeks to three months or full-time, plus potential revenue sharing.

Build full-stack products, AI agents, integrations, and automated workflows that improve business operations. Map processes, design secure architecture, develop backend and frontend systems, and rapidly launch MVPs for real users. Collaborate with technical and non-technical teams across India and international offices, while ensuring data privacy, security, reliability, scalability, and effective use of emerging AI tools.

Own the end-to-end product experience for agentic AI products, including roadmap, feature definition, UX optimization, onboarding, and in-app education. Analyze product usage and customer feedback to identify friction, simplify workflows, improve conversion, and prioritize high-impact improvements. Partner with design and engineering while independently driving product direction, creating detailed PRDs, tickets, prototypes, and designs in a fast-moving startup environment.

Develop and deploy multilingual automatic speech recognition systems for children’s reading assessment. Responsibilities span speech-data collection and annotation, audio preprocessing, model training and fine-tuning, decoding, forced alignment, error analysis, benchmarking, and deployment in low-bandwidth or offline environments. The role requires adapting modern ASR architectures, detecting reading errors, evaluating WER/CER and alignment quality, and collaborating with assessment and field teams to translate model outputs into reading fluency and accuracy measures.

Designs and develops scalable automated testing solutions for carrier-grade, cloud-native network products. Responsibilities include building functional, regression, performance, and end-to-end test frameworks; developing tests with Python, Java, or Go; integrating testing into CI/CD pipelines; applying AI/ML for test generation and defect analysis; analyzing network and test data; and collaborating with development, product, and QA teams.

Designs and develops scalable automated testing solutions for carrier-grade, cloud-native network products. Responsibilities include building functional, regression, performance, and end-to-end test suites; integrating testing into CI/CD pipelines; applying AI/ML for test generation, anomaly detection, defect prediction, and self-healing automation; analyzing network and test data; and collaborating with development, product, and QA teams.

Maintain and optimize production LLM and VLM services, including Flask/FastAPI microservices, Dramatiq queues, REST APIs, and Uvicorn/Gunicorn deployments. Design prompt strategies, feedback and evaluation pipelines, and AI routing integrations while monitoring latency, token usage, quality, and errors. Collaborate with engineering teams on scalable microservices, Dockerized deployments, CI/CD, security, and cloud infrastructure.