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 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

Work arrangement
No