Top Artificial Intelligence Jobs in Bengaluru

Develop and support SAP frontend solutions using HTML, CSS, JavaScript and SAP UI5. Work on S/4HANA transformation tooling integrating AI for automated test case, data, and script generation. Participate from backlog validation through realization, collaborate across SAP teams, identify risks, and deliver to specifications.

Lead development and optimization of astrodynamics algorithms for orbit determination, propagation, conjunction analysis, and prediction. Design, validate, and operationalize scalable, efficient SSA software; research ML/AI and numerical methods; mentor junior engineers; collaborate across software, data science, operations, and product teams; contribute to documentation, proposals, and publications.

Develop game concepts by translating requirements into efficient code, producing prototypes, and contributing to design and audio features, while ensuring game polish and maintenance.

As a Senior Backend Engineer, you will design and maintain scalable software solutions, embrace innovation with AI tools, and ensure high-quality engineering practices.

Design and ship robust frontend solutions and SDKs, own features end-to-end, collaborate with product and backend teams, mentor engineers, handle real-time/media streaming, implement authentication, offline-first sync, and debug end-to-end issues using network tools.

The LLM Ops Engineer will design, deploy, and manage scalable AI infrastructure for generative AI applications, focusing on document intelligence workflows and maintaining AI system health.

Design, build, and own automated GTM workflows and campaign pipelines; create and configure AI agents and internal self-serve tools; maintain funnel and pipeline data accuracy; instrument end-to-end reporting; and partner with sales, marketing, and leadership to turn GTM strategy into reliable, scalable systems.

The AI Engineer will build and optimize an enterprise search solution, manage a knowledge graph, develop ranking models, and enhance search relevance.

Lead global quality engineering and testing practices for AI-driven SaaS products, enhance automation, and ensure high-quality releases through effective strategies and collaboration.

Lead product quality across the SDLC by reducing defects, driving root-cause analysis, defining automation strategy and quality gates, building automated test frameworks (Playwright, API, integration, performance), integrating tests into CI/CD, enforcing release readiness, and using Claude Code and other AI tools to analyze issues and improve engineering outcomes.

The Staff AI Engineer will research, prototype, and develop advanced Generative AI solutions for enterprise applications, leveraging various AI models and frameworks.

The Lead Automation Engineer will develop and maintain automated testing frameworks for network observability, enhance product quality, and collaborate with engineering teams to improve testing strategies and practices.

Work on a TypeScript full‑stack product (Next.js/React) with an AI pipeline using Anthropic Claude, real‑time collaboration (Liveblocks/Yjs/Tiptap), and Cloud Run background jobs. Build UI features, improve AI agent prompts/schemas/output rendering, write and maintain unit tests, read docs to unblock yourself, and grow to own product slices.

Design and implement AI-enabled ServiceNow solutions across ITSM, FSM, and ITAM. Provide technical leadership and ensure best practices in AI capabilities, facilitate stakeholder workshops, and guide project teams.

Design and build realistic cloud and backend environments to evaluate AI-driven tasks. Create distributed-system scenarios covering deployment, troubleshooting, scaling, security, disaster recovery, observability, and validation tests. Produce reference implementations and faulty configurations, document architectures and procedures, and collaborate with technical leads to refine specifications.

Design and presales of end-to-end AI/HPC infrastructure: size GPU clusters, storage, networking, Kubernetes/container platforms, integrate liquid-cooled racks with facility constraints, validate solutions with OEMs, and support delivery for scalable, operable AI deployments.

Build and maintain Python/FastAPI backend services and React frontends, design and consume REST APIs, work with relational databases, and implement/debug AI-enabled features including vector DBs, embeddings, semantic search, and RAG workflows. Improve existing codebases, collaborate with product and design, participate in testing, deployment, and monitoring, and ensure performance, security, and maintainability.

Lead architecture and development of scalable backend and full-stack features for AI-native products, build agentic AI workflows and orchestration, design distributed systems and APIs, ensure security and performance, troubleshoot production, and mentor engineers.

Build and ship full‑stack product features, developer tools, and code‑intelligence workflows using React/Next.js, TypeScript, Node.js/Python, Postgres, and cloud services. Improve AI-assisted developer workflows, create repo‑aware copilots, automate testing/CI checks, review AI-generated code for security and performance, and document reusable patterns and engineering playbooks.

The Senior Backend Engineer will own feature development from architecture to production, focusing on AI-driven systems, data pipelines, and quality engineering within a fast-paced e-commerce company.

The Test Automation Engineer will design and maintain test automation frameworks for network observability solutions, collaborate with engineering teams, integrate tests into CI/CD pipelines, and drive quality and testing metrics.

Own and drive the frontend of a data-intensive SaaS product: design React/TypeScript architecture in a Turborepo, implement typed API contracts, build interactive visualizations, enforce AI-native engineering practices and automated tests, optimise performance and accessibility, mentor engineers, and contribute to CI/CD and frontend standards.

Freelance specialist to analyze semiconductor device behavior, review fabrication and process-integration methods, assess scaling, performance, yield and reliability, interpret technical data, identify gaps, and provide clear explanations and structured feedback to improve AI-generated microelectronics engineering content.

Review 3D CAD models and engineering drawings for manufacturability, GD&T correctness, and tolerance stack-ups; recommend DFM improvements; develop realistic design/manufacturing scenarios; explain engineering mechanics and tribology; and provide structured feedback to improve AI-generated engineering content.