Agentic AI Developer

Vibehackers; Hyderabad; On-site; INR 400,000 - 700,000; Full time

Job summary

Qentelli seeks an Agentic AI Developer to design production-grade AI agents and workflows for enterprise software development, testing, and operations. You will work at the intersection of LLM engineering, enterprise integration, and autonomous system design to help engineering teams ship faster with intelligent agents.

Responsibilities include building multi-step LangGraph agents, integrating LangChain components, optimizing prompts, and implementing human-in-the-loop review workflows.

Qualifications

• 5+ years Python development experience.
• 2+ years with LangChain and LangGraph; ability to design graph-based agent workflows.
• Experience with LangSmith for tracing, debugging, and evaluating LLM applications.
• Proficiency with LLM APIs and strong prompt engineering skills.
• Experience integrating agents with external APIs and databases via REST or SDKs.

Responsibilities

• Design multi-step, stateful LangGraph agents with branching, loops, and human-in-the-loop checkpoints.
• Integrate LangChain chains, tools, and retrievers into agent pipelines.
• Design prompts for structured outputs and reasoning tasks.
• Implement context management to optimize token usage and cost; evaluate LLMs per task.
• Build RAG pipelines with vector stores and embedding models for knowledge retrieval.
• Instrument agents with LangSmith tracing, evaluations, dashboards, and alerts.
• Connect agents to enterprise toolchains with secure deployments and audit logging.
• Collaborate with client teams to scope, build, and iterate agent solutions.

Skills

Python

LangChain

LangGraph

LangSmith

API Integration

Docker

AWS

Tools

Docker

Kubernetes

REST APIs

Job description

Directly working on agentic AI tooling (LangChain/LangGraph/LangSmith) and building AI developer tools and RAG pipelines; heavy focus on agent workflows and LLM-driven automation.

About the Role

Build production-grade agentic AI systems and multi-step agent workflows using LangGraph, LangChain, and LangSmith, focusing on LLM engineering, enterprise integration, and autonomous system design to help engineering teams ship faster with intelligent agents.

Job Description

Role

Qentelli is hiring an Agentic AI Developer to design and build production-grade AI agents and agent workflows for enterprise software development, testing, and operations. The role sits at the intersection of LLM engineering, enterprise integration, and autonomous system design.

Key Responsibilities

• Design and implement multi-step, stateful LangGraph agents with branching, loops, conditional routing, parallel execution, and human-in-the-loop checkpoints.
• Integrate LangChain chains, tools, and retrievers into agent pipelines.
• Design and optimize prompts for structured outputs, schema-conformant generation, and reasoning tasks.
• Implement context management strategies to control token usage and cost; evaluate and select LLMs per task (OpenAI, Anthropic, Gemini, open-source).
• Build RAG pipelines with vector stores and embedding models for domain-specific knowledge retrieval.
• Instrument agents with LangSmith tracing, evaluation datasets, and automated regression tests; build dashboards and alerts for performance, latency, cost, and failures.
• Implement human-in-the-loop review workflows, approval gates, and quality metrics; run structured evaluations before production.
• Connect agents to enterprise toolchains with secure, governed deployments (API key management, access controls, audit logging).
• Collaborate with client engineering teams to scope, build, and iterate on agent solutions; contribute reusable agent components, prompt templates, and internal agent library assets.
• Document agent architectures, design decisions, and runbooks for client handover.

Requirements (Required Qualifications)

• 5+ years of Python development experience; strong understanding of async, concurrency, and API patterns.
• 2+ years working with LangChain and LangGraph; demonstrated ability to design graph-based agent workflows with branching, loops, and state management.
• Experience with LangSmith for tracing, debugging, and evaluating LLM applications.
• Proficiency with LLM APIs (OpenAI, Anthropic Claude, or equivalent) and strong prompt engineering skills (structured outputs, chain-of-thought, few-shot, tool use patterns).
• Experience integrating agents with external APIs and databases via REST or SDKs.
• Solid software engineering practices: Git, CI/CD, testing, code review.
• Familiarity with vector databases and RAG implementations (Chroma, Pinecone, pgvector), embedding models, and re-ranking.
• Experience with containerization (Docker) and cloud deployments (AWS, Azure, or GCP).
• Ability to read and work with enterprise API documentation and data schemas.

Preferred Qualifications

• Experience building multi-agent systems or agent-to-agent communication patterns.
• Knowledge of evaluation frameworks such as RAGAS, LangSmith Evals, or custom harnesses.
• Contributions to open-source LLM or agent projects.

Python LangGraph LangChain LangSmith OpenAI Anthropic Claude Gemini Chroma Pinecone pgvector Docker AWS Azure GCP REST SDK Git CI/CD RAG RAGAS LangSmith Evals Embedding models

Skills

LLM Engineering Prompt Engineering System Design Async & Concurrency API Integration Observability Testing & CI/CD Containerization Cloud Deployment Vector DBs & RAG Documentation Collaboration Security & Governance