Software Engineer - Agentic AI & BackendSteps AI · On-Site, Hyderabad, India · Full time, Paid

About Steps AISteps 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 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 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 APIs OpenAI, Azure OpenAI, Anthropic, Google Gemini - direct integration and proxying patterns

Vector databases At least one of Milvus, Pinecone, Weaviate, or Qdrant - schema design, dense + sparse retrieval, batched upsert, idempotency

Embeddings & reranking Modern embedding models, dimensionality and quality tradeoffs, cross-encoder reranking

Workflow orchestration Temporal, Airflow, Prefect, or equivalent at production scale

Backend (Python) FastAPI, async I/O, Pydantic, structured logging

Backend (TypeScript) Node or NestJS — comfortable enough to ship full-stack features

Observability LLM tracing (Langfuse or similar), OpenTelemetry, error monitoring

Cloud + DevOps Docker, AWS or GCP, CI/CD, production debugging

Strong Plus

- Multimodal model integration - vision and audio
- Speech: transcription and streaming synthesis
- Layout-aware document AI and advanced structured extraction
- Multi-agent patterns beyond single-loop architectures
- Advanced retrieval research - late-interaction models, learned sparse retrieval, and similar
- Open-source contributions to agent frameworks or vector databases
- Published papers, patents, or conference talks in the GenAI / agentic space
- Domain expertise in e-commerce, govtech, healthtech, or financial services

Mindset We Hire For

- You think like a system architect, not a prompt engineer. Prompt-only "AI engineers" are not the right fit.
- You ship rapidly and iterate publicly. A new integration goes from idea to production in days.
- You read source code instead of asking Stack Overflow. When a framework behaves unexpectedly, you read its internals.

- You measure what you ship. Token usage, latency, retrieval quality, regression rates - all instrumented from day one.
- You take ownership of failures. When a customer reports a bad outcome, you trace it through prompt, retrieval, and tool output, then fix the root cause.
- You operate well in fast-paced, high-ownership environments. This is not a slow shop.

Qualifications

- B.Tech / M.Tech / MS in CS, Software Engineering, AI/ML, or a related field
- 2+ years of professional engineering experience with a strong production track record
- Demonstrable experience shipping agentic AI systems to real users
- On-site availability in Hyderabad - every working day, in person, with the team

📌 Software Engineer - Agentic AI and Backend (Hyderabad)
🏢 Steps AI
📍 Hyderabad