Senior GenAI Engineer

Job Description

• This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior GenAI Engineer based in India. This role offers the opportunity to engineer and operate production-grade generative AI systems used by real customers. You will take LLM-powered capabilities from initial concept through development, deployment, monitoring, and continuous improvement. The position combines backend engineering with agentic AI, retrieval-augmented generation, evaluation, and production observability. You will work hands-on with technologies such as LangGraph, Azure OpenAI, Gemini, Claude, and modern cloud services. A major focus will be ensuring high answer quality while optimizing latency, reliability, and infrastructure costs. You will join a technically ambitious environment where your decisions can directly influence how AI-powered products are designed and operated at scale.
• Accountabilities Build and deliver LLM-powered features end to end, including agentic workflows, retrieval pipelines, and tool-calling capabilities using LangGraph. Develop production-ready FastAPI services with streaming, persistence, automated testing, and appropriate reliability standards. Establish and maintain evaluation datasets, regression suites, and LLM-as-a-judge mechanisms to measure answer quality and validate changes before production release. Optimize the context provided to models by transforming user questions into effective search queries, orchestrating multi-step research workflows, and improving retrieval and prompt strategies. Diagnose incorrect or low-quality responses by determining whether issues originate from retrieval, query formulation, context construction, prompting, or model behavior, and implement targeted improvements. Instrument and monitor AI workflows using tracing and observability tools such as Langfuse and Azure Application Insights. Investigate production issues using real execution traces and proactively improve system reliability, performance, and answer quality. Manage latency, token consumption, and infrastructure costs, including intelligent routing across different model sizes and model families through an AI gateway. Contribute to architectural and technical decisions across the GenAI platform while identifying opportunities to improve automation, scalability, and operational efficiency. Collaborate with engineering and product stakeholders, communicate technical findings clearly, and contribute to the continuous evolution of production AI capabilities. Requirements 5+ years of professional software engineering experience, with strong hands-on experience building and operating backend services, APIs, asynchronous systems, and automated tests. Demonstrated experience taking LLM-powered features from prototype to production and maintaining them in a live environment. Practical experience with agentic AI or orchestration frameworks, ideally LangGraph. Strong hands-on experience with Retrieval-Augmented Generation (RAG), including retrieval strategies, query optimization, context construction, and evaluation. Experience debugging production LLM systems using tracing, evaluation frameworks, latency analysis, cost monitoring, and quality metrics. Professional experience deploying and operating cloud-based services, preferably within Microsoft Azure. Strong Python development skills, ideally combined with FastAPI and experience building scalable AI-enabled backend services. Familiarity with technologies such as Azure OpenAI, Google Gemini, Anthropic Claude, FAISS, PostgreSQL, Docker or Podman, and related AI infrastructure. Experience with infrastructure-as-code tools such as Bicep is an advantage. Strong analytical thinking, problem-solving ability, and sound technical decision-making skills. Fluent English communication skills, with the ability to collaborate effectively in a distributed and multicultural environment. Proactive, open-minded, and creative approach, with a genuine interest in advancing AI capabilities and automation. Previous mentoring or technical leadership experience is considered a plus. Benefits B2B contract arrangement. 100% remote work. Long-term engagement and the opportunity to contribute to a live production LLM product. Hands-on exposure to modern generative AI and LLM technologies. Work with a contemporary technology stack spanning agentic AI, RAG, cloud infrastructure, observability, and backend engineering. Flexible working environment designed to support autonomy and effective collaboration.
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