Software Engineer — Agentic AI & Backend

Software Engineer — Agentic AI & Backend Steps AI · On-Site, Hyderabad, India · Full-Time 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 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. Application form: https://forms.gle/mGF2DNVoNNmLxZsi9 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 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, 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 Compensation Top-of-market base, calibrated to live demonstration of capability Meaningful equity for a senior IC role Direct, no-gatekeeper access to the founders Full ownership of the AI roadmap within your scope from day one To Apply Resumes will only be reviewed if the application form is completed. Application form: https://forms.gle/mGF2DNVoNNmLxZsi9 The form takes 5 minutes. Submitting a CV outside the form will not be reviewed. Shortlisted candidates will be contacted within 48 hours of submission. Steps AI · stepsai.co · Hyderabad, India