Ai Integration Engineer

Job Title: AI Integration Engineer

Location : Hyderabad (Hybrid - 3 days WFO)

Employment Type : Full-time

Experience: 5 to 8 yrs

Position Overview It is seeking a highly skilled AI Integration Engineer to join its Agentic AI Task Force. This role focuses on integrating advanced AI models into core business operations by building scalable, reliable systems that enable autonomous AI agents to interact seamlessly with internal tools, data sources, and communication channels.

Must-Have

• Strong experience in LLMOps (monitoring, evaluation, optimization of AI systems)
• Hands-on expertise with LangGraph and Moltbot (or similar agent frameworks)
• Advanced proficiency in Python (FastAPI, Pydantic, async systems)
• Experience with Vector Databases (Pinecone, Milvus, Qdrant)
• Solid understanding of OAuth and secure API integrations

Key Responsibilities Design and develop APIs and tools to enable AI agents to interact with CRM systems, support platforms, and internal databases.

Build and optimize Retrieval-Augmented Generation (RAG) pipelines for accurate, context-aware AI responses.

Implement middleware to connect AI workflows with front-end interfaces such as chat and email systems.

Manage data ingestion, embedding, and retrieval processes within vector databases.

Monitor AI system performance, including latency, token usage, and cost optimization.

Deploy and maintain AI-powered microservices using CI/CD pipelines, ensuring system reliability and scalability.

Required Qualifications Bachelor’s degree in Engineering (B.E./B.Tech) or related field.

Strong expertise in REST APIs, Webhooks, and authentication protocols (OAuth).

Proficiency in Python (FastAPI, Pydantic) and experience with WebSockets/streaming.

Hands-on experience with vector databases (e.g., Pinecone, Milvus, Qdrant).

Experience with LLM providers (e.g., OpenAI, Anthropic) and AI frameworks (e.g., LangGraph, Moltbot).

Familiarity with LLMOps tools for monitoring performance and model behavior.

Additional Qualifications 1- Cognitive Architecture Design: Ability to design Multi-Agent Orchestration (MAO) patterns (e.g., Manager-Worker, Peer-to-Peer, or Hierarchical teams).

2- Advanced Prompt Engineering & Optimization: Mastery of DSPy (Programming instead of Prompting), chain-of-thought, and automatic prompt optimization.

3- Guardrail & Safety Engineering: Implementing frameworks like NeMo Guardrails or LlamaGuard to ensure agents don't hallucinate or leak sensitive trading data.

4- Evaluation (Eval) Frameworks: Building custom Eval suites using Ragas or TruLens to mathematically measure the accuracy and reliability of agent reasoning.

5- State Management: Expertise in managing long-term memory and persistent state across complex, multi-day agentic tasks.

6- Infrastructure: Experience with Docker, Kubernetes, and cloud-native serverless functions.