- Senior level: ~5+ years building production software, with ~4+ years specifically in applied ML or LLM/GenAI systems engineering. You own work end to end from an ambiguous problem to shipped in front of real users and stay responsible for it in production.
- Deep, hands-on production experience building LLM-powered application features against any major model provider or open-weight model we are provider-agnostic and screen on the capability, not the vendor.
- Retrieval-augmented generation (RAG) as a discipline you can speak to in your own words and have built: embeddings, vector search, retrieval ranking, and grounding/citation enforcement.
- Evaluation and guardrails treated as engineering, not an afterthought: prompts kept as versioned, tested artifacts with eval/regression checks (any framework or your own harness), plus structured/validated output enforcement.
- The production hardening that keeps AI accurate and trustworthy at scale: cost/latency optimization (caching, batching, prompt distillation), drift detection, and sanity bounds.
- Robust in Python OR TypeScript either qualifies; we are not requiring both.

What Were Looking For
- Has built and launched real products from start to finish. They can clearly describe what they built, who used it, and what they personally did.
- Uses AI coding tools every day to work faster, and can explain how they use them.
- Learns new things quickly. They can give an example of a tool or skill they picked up recently and used well.
- Works well on their own. They can take an unclear problem and move it forward without waiting for detailed instructions.
- Explains technical things clearly to non-technical people here, engineers work directly with the product team and the sales field.
- Is reliable: follows through without being reminded, raises problems early, and takes responsibility for the result,



not just the task.

- Own production LLM integration at maturity: structured, schema-validated output enforcement, drift detection, sanity bounds, and the cost and latency guardrails that keep in-product AI features accurate and trustworthy at scale.
- Treat prompts as engineering artifacts versioned, compared head-to-head, and protected by regression evaluations that gate changes before they ship.
- Design, build, and tune retrieval-augmented generation: embeddings, vector search, retrieval ranking, and grounding/citation enforcement so answers are accurate and never hallucinated.
- Build and operate the upstream content pipeline that authors and maintains the products use-case catalog scheduled, reproducible runs with viability scoring, a change-review workflow, and reliable write-back integration into the product.
- Partner with the product engineers to ship in-product LLM features recommendation generation, semantic search, and seller-prep with the right accuracy, cost, and latency profile.
- Own the quality and freshness of the catalog and the AI features so they do not go stale or degrade as the product scales, and explain model trade-offs in plain language to product and the field.

The AI Adoption Hub is Infors tool for sales teams to co-create industry AI adoption plans with customers, live in the meeting and the AI is what turns each conversation into that plan. Youll own applied LLM integration end to end: retrieval-augmented generation, evaluation, and the cost, latency, and quality guardrails that make AI features safe to ship. Hands-on engineering on a small, AI-first team production systems, not research.

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Software Engineer, Senior (Hyderabad)
🏢 Infor
📍 Hyderabad