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# Senior Technical Lead

- **Company:** HCL America, Inc.
- **Location:** United States
- **Experience:** Expert
- **Salary:** $78,000.0 - $148,000.0
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, JavaScript (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Audit Trail, Bash Shell, Databases, Continuous Integration, Data Masking, Data Stores, Database Queries, DevOps, Monitoring of Systems, Information Retrieval, Python (Programming Language), MongoDB, Nginx, NoSQL, OAuth, OpenID, OpenShift, Role-Based Access Control, Red Hat Enterprise Linux, Reverse Proxy, Search Technologies, Shell Script, Single Sign-On, TypeScript, Data Processing, Okta, ReactJS, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Parallel Computation, Generative AI, Backend, Agentic-AI, Git, Fastapi, LangSmith, Kubernetes, Milvus, Front End Software Development, Artificial Intelligence Governance, Api Design, Restful APIs, Model Context Protocol, Webhooks, Docker, Natural Language Generation, Web Api
- **Published:** October 27, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=bbef0c1e8ddb4a7e

## About the Role

* 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.

Must Have Skill

Database -NoSQL - MongoDB,Python,Red Hat OpenShift,Retrieval-Augmented Generation (RAG),Vector Databases, * 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.

## Description

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., * 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.