AI Jobs in India

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Contributors may design original computational biology problems simulating real biology research workflows; create problems requiring Python programming using libraries such as Numpy, SciPy, and BioPython; ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes; develop problems requiring non-trivial reasoning in bioinformatics, systems biology, and molecular modeling; base problems on real research challenges or practical applications from biology; verify solutions using Python with standard computational biology libraries; document problem statements clearly and provide verified correct answers.

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As a Product Backend Engineer, you will design and operate backend systems that enable AI capabilities to provide dependable product experiences. Responsibilities include collaborating closely with Product to prioritize customer-focused work and deliver reliable features quickly, designing and owning backend services and APIs for web applications, workflows, and integrations, modeling and managing data in Postgres and related data stores, building secure, multi-tenant, permissions-aware systems with appropriate auditing, implementing backend features using LLMs and agentic tools, collaborating with frontend, product, and design teams to define API contracts and ship features end-to-end, adding logging, metrics, and tracing for service observability and on-call readiness, improving performance and scalability by profiling and tuning, and participating in code reviews, technical design discussions, and an on-call rotation for the services you own.

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As a Product Backend Engineer, design and operate backend systems that enable AI capabilities to deliver seamless and dependable product experiences. Build secure, multi-tenant services, orchestrate interactions with large language models (LLMs) and agentic tools, and define backend architecture for expanding product offerings. Collaborate with Product to prioritize customer-focused work and deliver reliable features quickly. Design and own backend services and APIs supporting web applications, workflows, and integrations. Model and manage data in Postgres and related data stores to ensure low-latency and reliable user experiences. Build permissions-aware systems with appropriate auditing for enterprise and government customers. Implement backend features that interact with AI systems via robust, well-structured services. Collaborate with frontend, product, and design partners for solution design, API contract definition, and end-to-end feature delivery. Add logging, metrics, and tracing for service observability and on-call readiness. Improve performance and scalability by profiling, tuning, and refining service boundaries. Participate in code reviews, technical design discussions, and an on-call rotation for owned services.

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Design original computational mathematics problems that simulate real mathematical research workflows; create problems requiring Python programming to solve using libraries such as Numpy, SciPy, and Sympy; ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes; develop problems requiring non-trivial reasoning chains in areas including number theory, combinatorics, graph theory, and numerical analysis; base problems on real research challenges or practical applications from mathematical practice; verify solutions using Python with standard mathematical libraries; document problem statements clearly and provide verified correct answers.

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Contributors may design original computational mathematics problems that simulate real mathematical research workflows, create problems requiring Python programming to solve (using Numpy, SciPy, Sympy), ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks), develop problems requiring non-trivial reasoning chains in areas like number theory, combinatorics, graph theory, and numerical analysis, base problems on real research challenges or practical applications from mathematical practice, verify solutions using Python with standard mathematical libraries, and document problem statements clearly while providing verified correct answers.

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The AI Deployment Engineer serves as the primary technical subject matter expert on OpenAI Codex for a portfolio of customers, embedding deeply with them to enable their engineering teams and build coding workflows. They partner directly with customers to design and implement AI-enhanced development workflows, from rapid prototyping through scalable production rollout. The role involves building high-quality demos, reference implementations, and workflow automations using Codex as part of the development process. The engineer leads large-format workshops, technical deep dives, and hands-on enablement sessions to help engineering organizations adopt AI coding tools effectively and safely. They contribute technical content including examples, guides, patterns, and best practices to the OpenAI Cookbook to help the broader developer community accelerate their work with Codex. Gathering high-fidelity product insights from real customer deployments, they translate these insights into clear product proposals and model feedback for internal teams. The engineer influences customer strategy and decision-making by framing how AI coding tools fit into their software development life cycle, technical roadmap, and organizational workflows. They also serve as a trusted advisor on solution architecture, operational readiness, model configuration, security considerations, and best-practice adoption.

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Contributors are responsible for designing original optics problems that simulate real physics research workflows, ensuring the problems are computationally intensive and cannot be solved manually within reasonable timeframes, developing problems requiring non-trivial reasoning in mechanics, electromagnetism, thermodynamics, and quantum mechanics, basing problems on real research challenges or practical applications from optics and physics practice, and documenting problem statements clearly while providing verified correct answers.

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As a Senior Software Engineer on the AI Voice Agent team, you will work on real-time systems involving live audio streaming and latency optimization integrated with speech providers. You will build and improve conversation intelligence systems that manage LLM layers, including prompt construction, context management, function calling, and dialogue management to create natural, actionable phone conversations. You will develop the action framework allowing configurable API calls with branching logic and runtime execution, supporting tasks like data lookup and ticket creation during calls. You'll manage knowledge ingestion, storage, and retrieval to enhance agent memory and learning over time. You will collaborate with designers to enable customers to create, configure, test, and deploy voice agents through intuitive product experiences. Additionally, you will help develop evaluation frameworks, analytics, call quality metrics, and monitoring instrumentation, and participate in on-call rotation duties.

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Design, automate, maintain, and optimize Python ETL pipelines (Spark/Ray) for large-scale multimodal data. Build and maintain data cataloging, lineage, quality tooling, integrity verification, access controls, and lifecycle management systems. Provide guidance, internal tools, and documentation to colleagues on data best practices. Serve as a custodian of the company’s datasets, ensuring overall data health, quality, and discoverability.

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Contributors design original computational statistics problems that simulate real mathematical research workflows and create problems requiring Python programming using libraries such as Numpy, SciPy, and Sympy. They ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes (days/weeks), develop problems requiring non-trivial reasoning chains in areas like number theory, combinatorics, graph theory, and numerical analysis, base problems on real research challenges or practical applications from mathematical practice, verify solutions using Python with standard mathematical libraries, and document problem statements clearly with verified correct answers.