Description

Key Responsibilities

l   Translate Needs: Convert ambiguous client business problems into highly technical product specifications for engineering activities.

l   Manage Expectations: Communicate limitations transparently while proposing viable alternative architectures

l   Agent Architecture: Design and deploy multi-agent systems capable of autonomous planning, reasoning, reflection, and task execution

l   MCP Tooling Development: Build and maintain Model Context Protocol (MCP) servers and clients to securely expose data, file systems, and enterprise tools to LLMs.

l   Microservices Integration: Wrap AI agents, RAG engines, and vector stores into modular, scalable microservices using Docker and Kubernetes.

l   Tool & API Orchestration: Implement advanced tool-calling architectures to connect LLMs to production databases, external APIs, and internal software systems

l   Pipeline Optimization: Evaluate and optimize agentic workflows for token efficiency, latency, context-window usage, and decision-making accuracy.

Required Skills & Qualifications

l   Programming: Expert proficiency in Python or TypeScript.

l   AI Orchestration: Hands-on experience with LangGraph, CrewAI, AutoGen, or LangChain.

l   Model Context Protocol: Practical experience implementing or consuming open-source and custom MCP tools/servers.

l   Architecture: Deep understanding of distributed systems, REST/gRPC APIs, message brokers (Kafka/RabbitMQ), and microservice communication.

l   Cloud & DevOps: Strong experience with containerization (Docker) and deploying services to cloud infrastructure (AWS, GCP, or Azure).

l   AI Expertise: Experience on vector databases embeddings, semantic search, and RAG pipelines

Key Skills

Education

Any Gradute