Top Artificial Intelligence Jobs in Delhi

Leads end-to-end cloud and platform engineering consulting engagements, owning scope, governance, delivery quality, client relationships, and executive outcomes. Manages a 2–3 person engineering pod through staffing, coaching, and performance development. Oversees multi-cloud discovery across AWS, Azure, and GCP, develops prioritized recommendations and roadmaps, applies AI-driven solutions, and evolves repeatable Rapid Solutions playbooks, templates, and delivery standards.

Lead end-to-end cloud and platform engineering consulting engagements, owning scope, governance, client relationships, delivery quality, and executive outcomes. Manage and coach a 2–3 person engineering pod, oversee staffing and utilization, conduct multi-cloud technical discovery, and develop prioritized recommendations and roadmaps. Evolve Rapid Solutions offerings, playbooks, templates, and delivery standards while applying AI-driven solutions to improve efficiency and insight.

The Vendor Program Manager develops vendor marketing plans, manages product categories, coaches staff, and reports on marketing performance while ensuring profitability and alignment with vendor objectives.

Build the AI layer for SigNoz: implement agent orchestration, long-running execution, streaming APIs (SSE), and MCP server integrations to expose observability data to LLM agents. Ship production LLM features, design scalable distributed backends in Go and async Python, and drive projects end-to-end in a remote-first environment.

Design, develop, and deploy generative AI agents and end-to-end ML pipelines. Lead prompt engineering, LLM integration, model training, and productionization, collaborating with customers and stakeholders to deliver AI solutions.

Own strategy, roadmap, and execution for HighLevel VoiceAI. Deliver reliable inbound/outbound AI voice agents integrated with telephony, CRM, workflows, and analytics. Improve agent setup, call quality, compliance, APIs/webhooks, monetization, and developer extensibility. Partner with engineering, design, support, and GTM to drive adoption, instrumentation, and measurable business outcomes for SMBs and agencies.

Lead full-stack development using Python and FastAPI; build high-performance APIs integrated with AWS (SQS, Lambda). Implement front-end using React/Angular, manage Mongo/MySQL/Oracle databases, integrate AI features via prompt engineering and GitHub Copilot/Cursor, optimize performance, apply DevOps/CI-CD, troubleshoot and collaborate in Agile/Scrum with Jira and Git.

Design and lead GenAI and agent-based AI solutions for large-scale modernization programs. Define prompt engineering, orchestration, validation, guardrails, and human-in-the-loop workflows. Apply AI to legacy systems (COBOL, HP NonStop, C++) for code comprehension, dependency mapping, documentation, and test generation. Collaborate with solution and platform architects, work in Agile sprint-based delivery, and guide distributed offshore/onshore teams.

Design, build, and deploy production-grade AI automation and agentic workflows for payments processes (reconciliations, audit and reporting). Integrate AI systems into operational pipelines to automate high-effort financial operations while preserving control, audibility, and reliability.

Lead and operate enterprise Azure and Microsoft 365 platforms, manage hybrid identity (Entra ID/AD), deliver and govern AI solutions (Azure OpenAI, Azure ML, Copilot), provide L2/3 support, secure and govern cloud resources, manage on‑prem infrastructure, and drive vendor, project, and stakeholder engagement to scale AI initiatives.

The Technical Manager will lead the integration of generative AI in applications, overseeing development, ensuring compliance, and managing projects while collaborating with stakeholders.

Develop and optimize AI models for efficient inference on custom silicon. Build fine-tuning pipelines, implement acceleration techniques (quantization, sparsity, distillation), create benchmarking and profiling tools, and collaborate with hardware, compiler, and quantization teams to translate algorithmic improvements into real hardware gains.

Design polished, user-centric mobile and web interfaces for BJAK's AI-driven neobank products. Convert requirements into wireframes, flows, prototypes and high-fidelity UI. Maintain and evolve the design system, ensure accessibility and responsiveness, iterate using research and analytics, and collaborate with PMs and engineers to deliver pixel-perfect implementations across insurance, payments, investments and related financial services.

Build and deploy production-grade generative AI applications: design multi-agent systems and LangGraph workflows, implement advanced RAG with vector DBs, fine-tune and host LLMs, develop scalable FastAPI backends, optimize latency/cost, and containerize services for production.

Lead design and architecture of Generative AI solutions on Azure and OpenAI, build end-to-end data pipelines and cloud infrastructure, produce solution blueprints, collaborate with stakeholders, and implement data preprocessing, API deployment, monitoring, and analytics delivery.

Design, build, and scale production Generative AI systems: multi-agent workflows, advanced RAG pipelines, model fine-tuning and quantization, LLM integration and swapping, high-performance FastAPI backends, vector search and retrieval, containerized deployment, and monitoring for latency, cost, and reliability.

Design and implement end-to-end machine learning solutions: analyze use cases, build statistical and ML models, prepare and enrich data, implement scalable PySpark models in Databricks, and collaborate with product and engineering to productionize analytics for personalization and business insights.

Design, fine-tune, and deploy generative AI models (LLMs, diffusion, VAEs). Build data pipelines and APIs, integrate AI into production, optimize model performance and latency, and collaborate with cross-functional teams to deliver AI-powered solutions.

Serve as the bridge between client leadership and engineering to identify high-value AI opportunities, map current processes, design future-state solutions, write implementation-ready specifications, prioritize initiatives, align stakeholders, and stay close to builds to ensure delivered systems match business goals. Requires fluent Spanish and English and rapid, iterative delivery.

Remote product associate who partners with customers to discover business needs, design and implement AI-powered solutions end-to-end, and deliver production-ready implementations working closely with the SF product team. Role is customer-facing, involves live meetings and offline solution-building, and supports part-time or full-time engagement across Europe and the Americas.

Work with customers and the SF product team to gather requirements and implement end-to-end AI solutions. Normalize, transform, visualize data, build integrations with external APIs, and deliver practical automation and product feedback. Role is customer-facing and requires technical problem-solving and fast execution.

Work across growth, funnel analytics, user research, product QA, competitive market analysis, and ad hoc operational tasks. Own outbound/paid campaigns end-to-end, identify funnel drop-offs, run user interviews, test product releases, and drive fixes and launches.

Build data integrations and tooling that combine CRM, product telemetry, support, billing, and other signals to provide GTM teams with context, automation, and real-time augmentation. Enable account prioritization, allocation, and funnel conversion tracking to increase revenue and save time across sales, marketing, growth, and customer success.

Build and scale an AI-powered stock research platform: design data pipelines ingesting SEC and market data, develop backend APIs, implement semantic search with embeddings and vector DBs, improve production reliability and performance, and own features end-to-end from design through deployment and monitoring.

The Google Cloud AI Specialist will design and implement AI solutions using Google Cloud Platform to enhance decision-making and provide actionable insights, develop machine learning models, and ensure data quality.