Sr Software AI Engineer

Job description

They are looking for a Sr. Software AI Engineer who will write production code, own features end-to-end from spec through deployment, operations, and observability, and participate in on-call rotation for what the team ships. There is no handoff to a separate ops or reliability function; engineers own the full lifecycle. Test automation is built into the shipping process using AI-driven tooling and the golden path. Build the GenAI-powered product experiences and the shared AI platform infrastructure that powers them. This includes RAG pipelines over the catalog and customer reviews, LLM-driven personalization, a conversational Wellness Agent, agentic workflow systems, and the evals and MLOps layer that makes AI features production-grade and repeatable. Specializations within this track include: RAG and personalization, agent framework and tool use, evals and guardrails, and LLM application development for internal business functions such as marketing automation and BI agents. What you will do: Design, build, and operate production AI features: RAG pipelines, LLM-driven recommendations, conversational agents, or agentic workflow automation. Build the shared AI platform layer: retrieval infrastructure, eval frameworks, model monitoring, guardrails, and observability. Write LLM applications and integrations with marketing platforms, BI tools, or customer-facing product surfaces. Evaluate model and feature quality using structured eval frameworks; iterate on prompts, retrieval strategies, and model selection using data. Use AI-driven SDLC tooling such as Claude Code as a daily practice for both AI and non-AI code. Coordinate with the Personalization team to align GenAI product features with existing ML personalization signals. Document AI system design decisions, evaluation results, and operational lessons in the shared knowledge base. Own the observability of AI systems you build: latency, cost, quality drift, and error rates; participate in on-call rotation and respond to production incidents. 8+ years of software engineering experience. Fully autonomous; drives technical decisions within the team; mentors junior engineers. Python proficiency; comfortable building and operating production LLM applications. Hands-on experience with at least one specialization: RAG and retrieval systems, LLM evaluation, agentic frameworks (LangChain, LlamaIndex, or similar), or LLM-based workflow automation. Understanding of prompt engineering, context window management, and LLM output quality tradeoffs. Familiarity with vector databases, embedding models, or semantic search. AI-driven SDLC : hands-on experience shipping production code with AI-assisted development tools such as Claude Code, GitHub Copilot, or Cursor. The bar is not awareness; it is daily use in delivering real software. Full-stack awareness: comfortable contributing across layers of the stack when needed; purely single-layer specialists are not the target profile. Production ownership: experience owning features end-to-end from spec through deployment, NICE TO HAVE Exposure to MLOps tooling or model deployment pipelines. Contributions to internal developer tooling, golden path standards, or SDLC process improvements. Experience with e-commerce platforms, product catalogs, or high-traffic consumer applications. Exposure to MLOps tooling or model deployment pipelines. Experience working in distributed teams across different time zones / geographies. Track record of documenting architectural decisions, writing RFCs, or contributing to engineering wikis. Selected candidates will be invited to take part in several rounds of interviews. The role is expected to be full time and ideal candidates should be looking for a long term engagement. A background check will be required as part of the onboarding process. Client is one of the world's largest e-commerce retailers of health, wellness, and beauty products, serving customers in more than 185 countries. With a catalog of over 30,000 products and global logistics infrastructure, they help millions of people live healthier lives every day. They are growing its engineering organization around an AI-first mandate: using AI not just as a product feature but as the foundation for how they build software, serve customers, and operate the business.