Job Description

Company Description

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.

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.

Qualifications

• 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.

Additional Information

• 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.