We are seeking a skilled Agentic AI Developer to design, architect, and deploy intelligent, autonomous AI agent systems. In this role, you will go beyond traditional static LLM applications to build dynamic agents capable of multi-step task decomposition, reasoning, tool execution, state management, and real-time environment adaptation.

Key Responsibilities

1. Agent Architecture & Orchestration

- Design and implement autonomous single-agent and multi-agent systems capable of multi-step task planning, reasoning, and execution.
- Implement structured reasoning frameworks, such as Chain-of-Thought (CoT), ReAct, and Tree-of-Thoughts.
- Build short-term and long-term memory architectures using vector databases, key-value stores, and graph-based context engines.

2. Integration & Function Calling

- Connect AI models to external enterprise applications, APIs, databases, and third-party tools via function calling, MCP (Model Context Protocol), and REST interfaces.
- Build dynamic retrieval systems (RAG, GraphRAG) to ground agent reasoning in private context and real-world data.

3. Safety, Guardrails & Human-in-the-Loop (HITL)

- Implement safety layers, output parsers, and validation frameworks to prevent agent hallucination, prompt injection attacks, and unintended actions.
- Architect Human-in-the-Loop (HITL) approval gates for sensitive operational actions (e.g., financial execution, database modifications).

4. Evaluation, Observability & Performance

- Establish evaluation metrics for agent behavior, reasoning accuracy, tool use efficacy, and response latency.
- Utilize tracing tools (e.g., LangSmith, Phoenix, Datadog) to debug agent loops, optimize token/API usage costs, and identify execution bottlenecks.

Required Qualifications & Skills

Technical Core





- Programming: Advanced proficiency in Python (async/concurrency, type hinting, Pydantic) and modern API backend engines (FastAPI, Flask).
- Agent Frameworks: Experience with agent framework ecosystems like LangGraph, AutoGen, CrewAI, LlamaIndex, or Semantic Kernel.
- LLMs & APIs: Deep understanding of foundational LLMs (OpenAI GPT, Anthropic Claude, Google Gemini, open-source models like Llama) and fine-tuning/prompt engineering tactics.
- Vector Databases & RAG: Hands-on experience with vector stores (Pinecone, Qdrant, Chroma, Weaviate, Pgvector) and semantic search architectures.
- Databases & Tooling: Strong knowledge of SQL/NoSQL databases, Git, Docker, and REST/GraphQL APIs.

Soft Skills & Mindset

- System Design Thinking: Ability to decompose ambiguous human tasks into deterministic workflow loops.
- Product-Focused: Focus on building safe, usable, and high-utility features rather than speculative experimental code.

Preferred / Nice-to-Have Qualifications

- Experience with MCP (Model Context Protocol) or building custom API tools for LLM agent integration.
- Familiarity with TypeScript / Node.js for client-side or microservice integrations.
- Experience deploying containerized workflows on cloud platforms (AWS, GCP, Azure) with Kubernetes.
- Open-source contributions to agentic AI libraries or AI evaluation frameworks.

What We Offer

- Market-competitive salary and performance-based equity/stock options.
- Hardware budget for local LLM prototyping (Apple Silicon, dedicated GPU access).
- Learning & Development stipend for AI conferences, certifications, and research papers.

Pay: From ₹20,000.00 per month

Perks:

- Flexible schedule

Experience:

- Agentic AI: 1 year (Preferred)

Work Location: In person

📌 Agentic AI Developer / Engineer (Talavadi)
🏢 ENZO Solutions
📍 Talavadi