Senior Technical Lead

Job Summary

We are seeking a highly skilled Senior AI Engineer to lead the development of our next-generation Agentic AI systems. In this role, you will design, build, and scale advanced AI pipelines, focusing on complex RAG techniques, hybrid retrieval, and multi-agent workflows. You will work across the full stack-from engineering robust backend APIs to deploying scalable, secure AI solutions using Kubernetes and Docker.

You will play a critical role in bridging enterprise data with AI capabilities using the Model Context Protocol (MCP), while ensuring our systems are highly performant, secure, strictly governed, and free from hallucinations.

Key Responsibilities

AI Engineering & Agent Development

• Pipeline Design: Design and run automated pipelines for RAG and AI Agents, including advanced hybrid RAG (vector + keyword retrieval).
• Agentic Workflows: Develop multi-step agents built on LangGraph for complex tasks like document classification and multi-step reasoning. Develop the full stack and integrations for Agentic AI systems.
• Hallucination Control & Grounding: Ensure outputs are grounded in context through source-cited responses, retrieval relevance checks, and checking model outputs against trusted enterprise data.
• Multi-Modal Processing: Process text, images, and documents (PDF/DOCX) utilizing advanced vision, text, and embedding models.
• Prompt Engineering: Implement robust prompt engineering and systematic prompt management.

MCP Integration & Tooling

• Tooling & Integrations: Build Model Context Protocol (MCP) tools, servers, and integrations to securely expose enterprise data to AI assistants and agents (e.g., Claude).

System Architecture, Scalability & Backend

• High-Performance Systems: Build highly scalable systems utilizing async and batch-parallel processing, caching mechanisms, and tuned vector search/database queries for massive document volumes.
• Traffic Management: Apply rate-limiting techniques including token bucket, sliding window, per-user/per-API quotas, and backoff/retry mechanisms for LLM and API calls.
• API Development: Develop robust, asynchronous REST APIs and webhooks (HMAC-SHA256 signed) using FastAPI.

Security, Governance & Observability

• AI Security: Protect against prompt injections, detect and mask PII, filter sensitive data from prompts and responses, and implement strict AI guardrails.
• Governance: Build robust AI governance frameworks utilizing SSO (Keycloak/OAuth2/OIDC), role-based access control (RBAC), audit logging, and systematic evaluation of model outputs.
• Monitoring & Observability: Monitor systems by tracking LLM latency, token usage, cost, response quality, and data drift using LangSmith, complete with alerts and observability dashboards.

Skill Requirements

Technical Requirements & Stack

Core Languages

• Python 3.11
• JavaScript / TypeScript
• Bash

Backend & APIs

• FastAPI, Uvicorn, Pydantic v2 / pydantic-settings
• httpx (HTTP/2), aiofiles, async I/O
• REST APIs and webhooks (HMAC-SHA256 signed)

LLMs & AI Frameworks

• Models: Diverse knowledge of LLMs including gpt-oss-120b (text), pixtral-12b (vision), multilingual-e5-large (embeddings) or similar equivalents.
• Frameworks: LangChain, LangGraph (agent orchestration with checkpointing), LangSmith (tracing).

RAG, Vector Search & Data Stores

• Milvus (pymilvus, langchain-milvus) or similar vector stores for vector storage and metadata-filtered search.
• LangChain text splitters for advanced chunking.
• MongoDB or similar NoSQL document stores.

Model Context Protocol (MCP)

• FastMCP and the MCP Python SDK.
• Building JWT-secured MCP servers that allow external AI assistants to utilize internal search tools.

Frontend

• React

Security & SSO

• Keycloak SSO (OIDC / OAuth2).
• Advanced PII masking, sensitive-data filtering, and application guardrails.

DevOps, Deployment & Infrastructure

• Docker, Kubernetes.
• Nginx (reverse proxy, SSL/TLS).
• RHEL 9 VMs, Git, and shell-script deployments.

Experience managing CI/CD and deployments across five environments (dev/test/stage/pre-prod/prod) using Methods of Procedure (MOPs) for releases.

Other Requirements

What We Are Looking For

• Proven experience building and deploying production-grade AI agents, LLM pipelines, and RAG systems.
• Deep understanding of asynchronous Python programming and scalable backend architectures.
• Hands-on experience with vector databases and semantic search techniques.
• A security-first mindset with experience implementing SSO, data masking, and LLM guardrails.
• Strong DevOps fundamentals with the ability to manage deployments across strict, multi-stage enterprise environments.