Top Artificial Intelligence Jobs in Delhi

The Partner AI Deployment Engineer will guide AWS engagements, shape technical strategy, build AI architectures, and mentor partners to drive successful AI deployments.

Lead the development of AI-powered customer support solutions, guiding a team to build scalable, reliable, and secure systems using LLMs and third-party integrations.

Own the architecture and delivery of production-grade generative and agentic AI systems, including LLM agents, RAG pipelines, multi-agent workflows, APIs, and cloud-native services. Establish AI engineering, evaluation, observability, guardrail, and LLMOps practices while mentoring AI/ML and backend engineers. Collaborate with Product, Data, Platform, Security, and Compliance teams to deliver scalable, secure, and business-ready solutions, and provide technical direction on GenAI strategy and architecture.

Leads the identification, development, testing, and scaling of AI and automation solutions across internal delivery processes and future customer-facing use cases. Builds automated test suites, scripts repetitive workflows, pilots generative AI and agentic tools, creates reusable automation assets, tracks measurable value, and enables teams through training and knowledge sharing. Collaborates with project managers, product owners, process owners, testing communities, and delivery teams to ensure maintainable, responsible, and broadly adopted solutions.

Leads customer-facing design and implementation of cloud, platform, infrastructure, data, and AI solutions. Responsibilities include cloud migration and modernization, Infrastructure as Code, CI/CD, Kubernetes, automation, GitOps, platform security, observability, and AI-assisted development. The consultant conducts workshops, creates reusable platform capabilities, enables responsible AI adoption, mentors engineers, supports proposals and pre-sales, and ensures secure, reliable, governed, and cost-effective production solutions.

Lead and align Engineering, ML, and Analytics to deliver scalable, reliable production AI and data systems. Own technical direction, architecture, MLOps, and platform reliability while partnering with Product, Sales, and Customers to ensure engineering work maps to measurable business outcomes.

Develop and maintain AI-assisted development capabilities for the Eclipse-based SmarTest semiconductor testing environment. Integrate and optimize GitHub Copilot, resolve Eclipse and integration defects, implement customer-specific features, and perform testing and quality assurance. Translate customer requirements into technical solutions, provide technical assistance, evaluate changes in AI and Eclipse technologies, and collaborate with Microsoft, Advantest teams, and external partners to improve developer productivity and user experience.

Own enterprise AI engagements from discovery through production deployment. Design, build, integrate, debug, evaluate, and harden agentic systems using LLMs, RAG, tool calling, workflows, and guardrails. Lead technical workshops, proofs of concept, solution scoping, and executive ROI discussions. Measure adoption, containment, accuracy, and cost outcomes; create reusable delivery assets; enable partners; and provide product feedback. The role is customer-embedded and requires sustained on-site presence during engagements.

Design and deliver intelligent platforms, automation solutions, and AI-driven capabilities. Analyze complex production systems, identify failure modes, and improve reliability, efficiency, productivity, and mean time to recovery through AI, automation, operational intelligence, and modern engineering practices. Build scalable cloud-native solutions using Kubernetes, APIs, MLOps, generative AI, and workflow orchestration while mentoring teammates and driving continuous improvement.

Manage multilingual data collection and Large Language Model evaluation projects, ensuring quality assurance and effective global team coordination.

Build and operate backend services and infrastructure for LLM- and voice-driven healthcare applications. Implement APIs, productionize LLM workflows, deploy cloud containerized services, and monitor performance and reliability end-to-end.

Evaluate and annotate physics content, create problem sets and technical documentation, and assess AI-generated responses for scientific accuracy. Contribute to dataset creation and provide expert feedback to improve AI model training. Collaborate remotely with project teams to ensure timely, high-quality deliverables and improve evaluation methodologies.

Contractor engineering SME to review and create technical engineering content for AI training: analyze documentation, develop examples and datasets, evaluate AI outputs for technical accuracy, and collaborate with teams to improve model performance and processes.

Contractor role to create original, high-quality financial documents (reports, analyses, models) with rich visuals (tables, charts, graphs) to train AI. Ensure accuracy, anonymize sensitive data, contribute long-form documents (10–100 pages), and clarify submissions as needed.

Remote contractor role to create and evaluate mathematical problems, proofs, and datasets to train and improve AI models. Assess AI-generated solutions for correctness, produce technical documentation, and collaborate with cross-functional teams to develop benchmark datasets and provide actionable feedback to improve model reasoning.

Build and maintain Python backend services and internal tools for AI/LLM applications. Integrate LLM APIs, design scalable REST APIs and async workflows, handle prompt management and model routing, monitor performance, debug distributed systems, automate data workflows, and collaborate to productionize AI features with emphasis on scalability, reliability, and safety.

Lead enterprise AI strategy and roadmap; build and scale cross-functional AI/ML teams; architect and deliver LLM, RAG, predictive and data-heavy AI products; establish MLOps, governance, cloud-native platforms (Azure/Databricks/AWS/GCP); drive product lifecycle, KPI tracking, security, and responsible AI practices.

Implement and customize ServiceNow ITSM and Service Portal solutions, perform integrations (REST, SOAP, MID Server), maintain Discovery and CMDB, develop and optimize server/client scripts (Business Rules, Script Includes, UI Policies), run workshops/demos, translate client requirements into user stories, mentor junior developers, and participate in Agile delivery cycles.

Lead design, customize, and implement ServiceNow ITSM, Service Portal, and ITOM solutions. Perform integrations (REST, SOAP, MID Server), Discovery and CMDB maintenance, and develop/optimize scripts. Run workshops, mentor junior developers, and participate in Agile delivery to translate client requirements into scalable ServiceNow solutions.

Develop, fine-tune, deploy, and optimize AI/ML models and LLM-based RAG systems using Python, AWS Bedrock, and major ML frameworks. Build scalable REST APIs and microservices with FastAPI or Flask, deploy containerized solutions on AWS, and manage CI/CD. Design databases and automated ETL pipelines, integrate cloud services, and document AI workflows while collaborating with cross-functional technical and product teams.

Design and implement AI-powered data pipelines and workflows combining LLMs, embeddings, vector search, and RAG to transform structured and unstructured data into business-ready outputs. Build APIs and full-stack integrations, produce Excel/PDF/Word deliverables, optimize relational and vector data stores, and prototype scalable AI solutions while contributing to engineering best practices.

Own product areas in BJAK's AI Neobank app (insurance, payments, savings, investing, travel). Define problems, scope, success metrics and roadmaps; collaborate with engineering, design, data, ops, compliance and business teams; launch features, monitor performance, run experiments and iterate to improve user experience and business impact.

Lead product direction and roadmaps for major areas of BJAK's AI neobank app. Own prioritization, execution, launch quality, and metrics across multiple squads. Guide PMs, designers, and engineers, turning regulatory and operational constraints into practical product decisions across payments, savings, investing, insurance and travel.

Lead architecture and production of complex multi-agent LLM systems. Design evaluation pipelines (Evals), optimize RAG and vector search for scale, manage cost and latency tradeoffs, translate product requirements into agent behaviors, and mentor junior AI engineers.

Design and implement semi-autonomous AI agents and orchestration layers, integrate models with third-party tools, build feedback/learning systems, and maintain LLM pipelines and MLOps for enterprise environments.