Top Artificial Intelligence Jobs in Delhi

Design, develop, and deploy production-grade AI applications focused on agentic AI, multi-agent systems, RAG, and cloud solutions. Build Python and FastAPI backends, integrate LLMs and enterprise data, deploy applications with Docker and Kubernetes, and implement evaluation, observability, security, reliability, and responsible AI practices. Collaborate with product, engineering, and business teams to deliver scalable AI solutions.

Develop, train, fine-tune, evaluate, and deploy machine learning models using large datasets. Responsibilities include data preprocessing, feature engineering, deep learning, NLP and LLM research, predictive analytics, production API integration, experimentation, and model optimization. The role collaborates with software engineers, supports MLOps practices, and evaluates model accuracy, efficiency, latency, and performance.

Develop R automation and AI solutions supporting pharmaceutical clinical trials. Interpret requirements, identify workflow improvements, build proof-of-concept tools using R, Shiny, and LLM-supported coding, and mature successful prototypes into validated, documented, reusable solutions. Collaborate with statisticians, programmers, data scientists, and automation leads while ensuring compliance with clinical development standards, including testing, traceability, audit trails, version control, and regulated delivery expectations.

Build, tune, deploy, and validate machine learning models for fraud detection, risk scoring, and customer analytics in the BFSI and credit card domain. The role involves performance and stability testing, documentation, troubleshooting data and model issues, communicating findings to varied stakeholders, managing analytical projects independently, and staying current with emerging AI and ML techniques.

Design, build, and scale production AI agent workflows using AWS Bedrock AgentCore, Step Functions, Claude, MCP, RAG, and guardrails. Develop Python/FastAPI backends, agent-facing interfaces, document-processing pipelines, enterprise integrations, and AWS infrastructure. Implement observability, evaluations, security controls, human approval workflows, and CI/CD. Work directly with enterprise customers and nontechnical stakeholders to deliver end-to-end AI solutions with minimal guidance.

Leads enterprise AI architecture and transformation across hybrid and multi-cloud infrastructure environments. Designs Generative AI, AIOps, automation, governance, security, and self-healing infrastructure solutions using platforms such as Azure, AWS, and GCP. Develops RAG architectures, AI assistants, and operational workflows integrated with ServiceNow and enterprise systems. Advises executive stakeholders, leads large transformation programs, and supports solutioning, presales, governance, and customer-facing consulting.

Design and develop enterprise generative AI and RAG applications using LLMs, embeddings, vector databases, hybrid search, and agentic workflows. Build ingestion, retrieval, evaluation, observability, and secure API services; optimize accuracy, latency, scalability, and cost. Integrate AI solutions with enterprise systems and collaborate with technical and business stakeholders. Apply prompt engineering, context management, guardrails, security, and responsible AI practices while troubleshooting and improving production applications.

Lead the architecture and development of production-grade multi-agent systems and AI-powered security chatbots. Build applied LLM workflows for alert triage, summarization, and automation; create evaluation harnesses; monitor and improve production behavior; establish standards for retrieval, context engineering, tool design, and safety; and embed security controls such as privacy, access controls, and prompt-injection defenses. Collaborate across data science, engineering, product, and platform teams to move AI capabilities from prototype to production.

Evaluate and improve the safety, security, and robustness of large language models. Responsibilities include designing jailbreak, prompt injection, adversarial, and tool-use abuse tests; developing regression suites and evaluation frameworks; identifying vulnerabilities; translating findings into safety recommendations; and documenting methodologies and results for technical and non-technical stakeholders.

Build and own Supabase’s internal AI execution platform, including event-driven agent runtimes, durable state, human review, rollback, observability, evaluations, governance, and least-privilege tooling. Develop production agents, MCP integrations, infrastructure on Python and GCP, CI evaluation gates, operating metrics, and return-on-investment instrumentation. As the sole platform engineer, independently make architecture decisions, operate systems end to end, and ensure unsafe actions are structurally impossible.

Lead Supabase’s AI-native operating system by defining maturity models, company-wide standards, assessment processes, operating cadences, and transparency mechanisms. Manage two senior contributors focused on platform and enablement, drive adoption without direct authority, replace unnecessary meetings with automated reporting, integrate standards into onboarding, and measure whether AI agents reduce organizational coordination costs.

Design and deliver production-grade AI/ML architectures for education and assessment platforms. Translate stakeholder requirements into secure, cost-aware blueprints covering RAG, multi-agent systems, LLM judging, OCR, human review, cloud infrastructure, LLMOps/MLOps, observability, governance, and evaluation. Lead workshops, architecture decisions, production readiness, FinOps, sovereign hosting, and technical mentorship across international government and education programs.

Architect and build production-grade agentic systems and shared AI platform services at enterprise scale. Develop agent orchestration, tool and MCP integrations, LLM gateways, evaluation frameworks, observability, safety controls, and cloud infrastructure. Drive reliability, cost optimization, AI-first engineering practices, technical standards, cross-team alignment, and mentorship while remaining hands-on with coding, architecture, and production debugging.

Lead the architecture and hands-on development of an AI-powered React application builder. Design scalable distributed systems, LLM pipelines, RAG and embeddings workflows, code-generation systems, publishing infrastructure, and AI guardrails. Improve observability, reliability, latency, safety, and fault tolerance while mentoring engineers and partnering with product, design, infrastructure, and security teams. This is an individual contributor role requiring production coding, system design leadership, and operational excellence.

Leads Singapore-China digital economy collaboration, technology policy engagement, and strategic partnerships. Responsibilities include monitoring China's technology landscape, developing engagement strategies, advising senior management, building government and industry relationships, identifying G2G and B2B opportunities, coordinating bilateral workstreams, and representing IMDA in regional engagements. The role is based in China after an initial Singapore assignment and includes supervision of regional operations.

Leads global AI-first MarTech engineering strategy, architecture, governance, compliance, and delivery. Oversees scalable digital platforms, public-facing properties, GenAI applications, R&D, proof-of-concepts, and automated compliance capabilities. Builds and mentors a global team of engineering and AI professionals, manages vendors, and partners with marketing, business, cloud, security, privacy, infrastructure, and legal stakeholders to deliver secure, high-performance digital experiences.

Leads the architecture, development, deployment, and scaling of enterprise AI solutions for finance automation. Integrates LLMs with SAP, Salesforce, and cloud platforms; establishes prompt engineering, CI/CD, security, governance, monitoring, and data practices. Mentors developers, leads technical strategy and vendor evaluations, presents to executives, and drives reliable AI platforms supporting financial processes, analytics, reporting, and global operations.

Lead design and build of an end-to-end AI contract-review agent: prompt orchestration, RAG pipelines, fine-tuning, web UI and tooling, data/model infra, deployment (CI/CD/cloud), and product-driven features like assumption mapping and contract diagnostics.

Build and lead the AI engineering platform for enterprise COBOL modernization. Responsibilities include designing transformer models for COBOL-to-Java/Python translation, developing high-performance code analysis engines, optimizing inference, architecting distributed cloud-native systems, and delivering reliable AI pipelines for large enterprise codebases. The role also includes hiring and leading engineering teams, shaping product strategy, and partnering with the CEO as a technical co-founder.

Owns operational execution and scale-up of an AI agriculture program in India. Responsibilities include building work plans, coordinating multi-state delivery, managing government relationships, establishing state operations, solving logistical challenges, developing scalable systems, hiring and onboarding field staff, and supporting strategic pivots. The role serves as a founding team member and requires strong project management, adaptability, communication, relationship-building, and delivery skills in a fast-growing environment.

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.

Lead technical implementation of Claude Enterprise and Claude Cowork for a financial services customer. Design enterprise AI architectures, integrations, workflows, and secure scalable solutions; guide engineers through deployment and adoption; ensure regulatory, privacy, and governance compliance; identify high-value asset management use cases; collaborate with stakeholders; mentor engineers; and develop reusable AI frameworks and implementation standards.

Design, develop, test, and maintain production software for semiconductor test systems. Integrate AI and LLM capabilities into Java- and C++-based platforms, develop Python and Shell automation tools, define system interfaces, and evaluate integrations for performance, reliability, security, and maintainability. Collaborate with architects and AI specialists, apply engineering best practices, lead integration projects, and provide technical guidance in Linux environments.

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.