Top Artificial Intelligence Jobs in Delhi

Leads quality assurance for AI training data by managing review queues, auditing annotations, monitoring quality trends, coaching reviewers, clarifying guidelines, training new team members, identifying recurring errors, and escalating complex issues. The role also supports corrective actions and continuous improvement while ensuring accurate, consistent, and timely project delivery.

Coordinate workforce planning and deployment for over 1,800 contributors across AI training projects. Allocate resources based on project demand and capacity, maintain deployment and availability records, monitor onboarding and throughput, identify conflicts and shortages, track requests, coordinate with project and training teams, conduct shift handoffs, and improve resource-planning processes and workflows.

Leads AI data annotation teams through project delivery, translating client guidelines into workflows, setting production targets, monitoring quality and productivity metrics, coaching team members, managing staffing and deadlines, and resolving operational risks. Partners with QA and tooling teams to improve processes and maintain accurate, timely training data delivery in a fast-paced environment.

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.

Research Scientist responsible for building and advancing agent harnesses, tool use, context management, evaluations, scoring models, fine-tuning, and reinforcement learning. The role involves solving ambiguous applied AI research problems, designing experiments and evaluation infrastructure, improving LLM agent behavior, and taking research from hypothesis through production. Collaboration occurs with US-based AI leadership and an India-based applied AI team.

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.

Build and deploy AI-powered customer solutions across discovery, prototyping, and production. Responsibilities include designing secure multi-tenant and hybrid architectures, integrating identity and access management, encryption, networking, data engineering, CI/CD, observability, and production support. The role partners directly with enterprise customers, executive sponsors, and cross-functional teams to solve ambiguous problems, demonstrate prototypes, and standardize successful solutions. Regular domestic and occasional international travel is required.

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.

Lead the architecture, development, and deployment of enterprise AI systems using LLMs, RAG, multimodal models, and multi-agent architectures. Drive prototyping through production delivery, establish LLMOps practices, and ensure model reliability, security, and governance. Serve as a technical advisor during presales, collaborate with product and data teams, and mentor junior engineers.

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.

Provides systems safety engineering for aircraft turbine engines and power systems. Conducts hazard, reliability, and root cause analyses; develops safety requirements, plans, procedures, and reports; reviews designs for regulatory compliance; manages requirements using DOORS; supports certification and qualification; collaborates cross-functionally on risk mitigation, reliability metrics, process automation, and AI-enabled safety improvements.

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.

Investigate escalated production incidents (DB anomalies, failed payments, data migrations), write post-mortems and runbooks, and build AI-powered automation (LLM diagnostics, agents) plus developer tooling. Maintain CI/CD, GCP infrastructure, container deployments, observability, and partner on ERP and payment provider integrations.

Provides technical leadership for FAA and EASA certification and qualification of aircraft turbine engines and auxiliary power units. Prepares and reviews certification and qualification plans, reports, and related artifacts; coordinates with aviation regulators; standardizes and improves certification processes; and applies automation and artificial intelligence to reduce cycle time and cost. Supports systems engineering activities, talent development, technical coaching, and innovation across Honeywell Aerospace programs.

Build production AI/ML systems for extracting structured data from complex documents. Responsibilities include document ingestion, OCR, confidence modeling, RAG knowledge bases, agentic workflows, human-in-the-loop review, reprocessing and diffing, model optimization, evaluation, monitoring, and governance. The role also provides technical mentorship to mid-level AI engineers and owns systems end to end.

Designs and deploys production-grade AI agents, RAG systems, multi-agent workflows, SDKs, and scalable LLM applications. Builds secure knowledge architectures, integrations, MLOps and LLMOps pipelines, governance controls, and evaluation frameworks. Serves as a technical authority in client engagements, mentors engineers, supports presales and recruitment, and drives engineering standards across AI projects.