Software Engineer - Agentic AI and Backend

Software Engineer - Agentic AI & Backend

Steps AI · On-Site, Hyderabad, India · Full-Time, Paid

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.

The Role

We are hiring a Software 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.

Resumes will only be reviewed if the application form is completed.

What You Will OwnAgentic 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 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, checkpointing, streaming, human-in-the-loop

LLM AP…