Generative AI Engineer

Role: GenAI engineer

Type: Full time, Paid

Location: On-Site, Hyderabad, India

About Steps AI

Steps AI is an agentic AI platform that powers customer-facing AI agents for businesses across e-commerce, SaaS, healthcare, real estate, EdTech, and financial services. Our agents go live in under five minutes from a single URL, learn the business by ingesting websites, documents, FAQs, and product catalogs, and deploy across web, messaging, and social channels.

We are profitable, growing fast, and shipping production AI to customers every week.

The Role

We are hiring a GenAI Engineer to own the agentic AI layer of our platform — the brains of every conversation our agents have. You will design and evolve our agent runtime, integrate new tools and channels, advance our retrieval and memory systems, and own end-to-end LLM observability.

This role is AI-focused but not AI-only. You will also contribute to the surrounding backend services and data pipelines that power the AI layer. You will work alongside a peer Software Engineer focused on the platform side; together you will set the technical direction for major product areas.

What You Will Own Agentic AI Systems (Primary)

• Agent runtime & control flow - design and evolve how our agents reason, route, recover, and hand off; build robust patterns for human escalation, partial-state resumption, and graceful tool failure

• Persona & behaviour engineering

  shape how agents adapt across channels, business verticals, and brand voices; encode domain guardrails, retrieval mandates, and structured-output contracts

• Multi-provider LLM orchestration

  abstract across leading model providers with per-persona routing, fallbacks, retries, and cost/latency tradeoffs

• Tool ecosystem

  extend, debug, and ship integrations across CRMs, helpdesks, calendars, productivity suites, e-commerce backends, logistics carriers, messaging platforms, and internal services. A new provider should reach production in days, not weeks

• Retrieval & ranking

  own the hybrid retrieval stack across vector and lexical signals, including reranking, recall/precision tuning, and ingestion-time quality controls

• Memory systems

  long-term cross-session memory and short-term per-thread state, with clear contracts between them

• Streaming protocols

  token-level streaming, intermediate event emission, interruption and resume semantics, and structured terminal events for client integration

Workflow Orchestration & Data Engineering

• Long-running workflows for ingestion, crawling, FAQ and Q&A synthesis, product discovery, sentiment work, outbound campaigns, and channel onboarding

• Queue & worker design

  separation of heavy and light work, concurrency control, retry semantics, and end-to-end tracing

• Embedding & indexing pipelines

  async batched writes, idempotent reindexing, provider rate-limit safety, and intermediate object-store staging

• Document understanding

  multi-format parsing, OCR for scanned content, and structured extraction

• Web acquisition

  adapter-based crawling with rate limiting and resilience; pluggable backends for varied site profiles

• Provider-agnostic embedding layer

  designed so new embedding models drop in without touching downstream consumers

AI / Backend Integration

You will not throw work over the wall. You will go into the backend stack when needed.

• The agent configuration pipeline that turns persona definitions into runnable behaviour
• New API endpoints powering AI-driven features — summarisation, FAQ synthesis, recommendation flows, and more
• Inbound webhook flows from commerce platforms and messaging providers that drive agent behaviour
• The plumbing that ties skill and tool configuration across our service boundaries

You Lead. You Don’t Just Code.

• Own the AI roadmap for your area and propose what to build next
• Mentor and review the work of junior engineers as the team grows
• Run weekly architecture reviews with the founders
• Defend tradeoffs in writing through RFCs, design docs, and postmortems
• Be the on-call escalation point for AI / agent production issues
• Set the bar for code quality, testing rigour, and observability across the AI stack

Required Skills

You should have shipped these to production and be able to demonstrate them live:

Agent frameworks Production experience with LangGraph, LangChain, or comparable agent runtimes — tool calling, state machines, ch…