Agent Application Engineer (Backend / Agentic AI) — Level 1

About Sentiaflow

Sentiaflow is an AI engineering and IT services company building production-grade agentic AI systems, sitting at the intersection of LLMs and real business operations — data, APIs, permissions, workflow state, human decisions, security, and measurable outcomes. Our initial domain focus is healthcare, particularly clinical trials, where reliability, traceability, and clear human-decision boundaries matter more than a slick demo.

Job Description

As a Level 1 engineer, you'll implement bounded parts of a production agentic workflow under an Agent Captain or senior engineer, connecting models to application services, tools, data sources, and human approval points.

You will:

• Translate a scoped business workflow into typed inputs, outputs, states, actions, and escalation paths
• Build backend services and tool integrations (Node.js/TypeScript or Python)
• Use LLMs only where model judgment adds value; keep rules, validation, authorization, and workflow control in deterministic code
• Design and validate structured model outputs before they touch downstream systems
• Handle partial data, tool failures, duplicate events, retries, timeouts, rate limits
• Add logging, traces, metrics, and decision records for diagnosability
• Write tests and evaluation cases that check whether the full workflow behaves correctly — not just whether output sounds fluent
• Protect sensitive data; participate in code, design, and release-readiness reviews
• Explain implementation trade-offs clearly to engineers and stakeholders

Success in 6 months: own a bounded workflow module end-to-end, integrate models without letting probabilistic output bypass deterministic controls, produce release-ready evaluation evidence, and diagnose cross-boundary failures with less supervision.

Desired Skills

• Approximately 3–6 years hands-on backend/application engineering experience, with demonstrable hands-on work building agentic systems — not just calling an LLM API from a backend service
• LangGraph (or comparable agent orchestration framework) experience is required — building multi-step, stateful agent workflows with conditional branching, tool-calling loops, and recovery/retry logic, not a single-prompt wrapper
• Deep RAG experience, including:
• Chunking strategy design, embedding model selection, and retrieval evaluation (not just "connected a vector DB")
• Hybrid search (dense + sparse/keyword), re-ranking, and query rewriting/decomposition
• Handling retrieval failure modes: irrelevant context, stale data, contradictory sources, citation/grounding accuracy
• Measuring RAG quality (precision/recall on retrieval, faithfulness/groundedness of generation) — not eyeballing outputs
• Experience designing agent state machines / workflow graphs: tool selection, planning loops, human-in-the-loop interrupts, checkpointing, and state persistence across long-running workflows
• Strong programming in JS/TypeScript (preferred), Python, Java, C#, or Go
• Solid grasp of API design, databases, async processing, auth, testing, deployment
• Comfort reasoning about state, retries, idempotency, concurrency, permissions, audit trails, failure recovery
• Real production debugging experience, not just greenfield builds
• Clear technical communication

We're looking for engineers who've actually built and tuned agentic/RAG systems in production — not those who've only wired together frameworks or prompted an LLM API.

Nice to have: experience with other orchestration frameworks (CrewAI, AutoGen, custom state machines), observability/eval tooling (LangSmith, Langfuse, custom trace pipelines), healthcare or regulated-industry background. Bachelors from IIT or NIT highly preferred.

Skills

LangGraph, Web API, Information architecture, knowledge Graph, Semantic search