Rekruton Technologies

What You'll Do ● Independently design, build, deploy, and operate AI-powered features, APIs, services, and workflows. ● Develop production LLM applications using structured outputs, tool/function calling, RAG, agentic workflows, and context-management patterns. ● Design retrieval pipelines across structured and unstructured enterprise data, including chunking, embeddings, vector/hybrid search, ranking, and&nbsp;grounding strategies. ● Evaluate and integrate models from multiple providers based on quality, latency, reliability, security, and cost trade-offs. ● Build reusable components within your pod's codebase for prompts, tools, retrieval, and evaluations, and flag candidates for cross-pod standardization. ● Define and maintain automated evaluations and regression tests for AI quality, groundedness, task success, and failure modes. ● Implement production controls including observability, caching, retries, fallbacks, rate limits, human-in-the-loop flows, and cost controls. ● Troubleshoot production issues and improve reliability, latency, quality, and operating cost using measurable signals. ● Participate in technical design reviews and contribute to AI engineering standards and documentation. ● Support and mentor AI Engineer I teammates through reviews, pairing, and knowledge sharing. Technical Skills &amp; Technology Stack ● Programming: Strong Python and software engineering fundamentals; working knowledge of Java or JavaScript/TypeScript is useful for platform&nbsp;integration. ● Data: Strong SQL; Snowflake or equivalent cloud data platforms; structured and unstructured data processing. ● Generative AI / LLMs: Hands-on experience with major model APIs, structured outputs, tool/function calling, model selection, and prompt/context&nbsp;design. ● AI Application Development: RAG, embeddings, vector/hybrid search, retrieval/ranking, agentic workflows, context management, and basic memory&nbsp;patterns. ● Frameworks: Practical experience with LangGraph, LangChain, LlamaIndex, or equivalent orchestration libraries; ability to work without framework&nbsp;lock-in. ● APIs &amp; Integration: REST APIs, JSON, asynchronous/service integration patterns; familiarity with MCP and tool ecosystems is preferred. ● Cloud &amp; Engineering: AWS or equivalent cloud, Docker, Git, CI/CD, automated testing, logging, and production debugging. ● Production AI: Evaluations, observability/tracing, guardrails, caching, fallbacks, latency optimization, and token/cost management.