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# Senior Technical Lead

- **Company:** HCL Technologies
- **Location:** Tampa, FL, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Software Applications, Microsoft Azure, Cloud Computing, Continuous Integration, Distributed Systems, Knowledge Management, Machine Learning, Language Modeling, Natural Language Processing, Performance Tuning, OpenAI, Search Technologies, Web Platforms, Cloud Platform System, LangChain, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, Llamaindex, Generative AI, Agentic-AI, AI Platforms, Machine Learning Operations, Claude, Virtual Agents, Semantic Kernel, Automation Anywhere, Microservices
- **Published:** November 4, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-technical-lead-tampa-fl--9b39a650-ebf2-4fda-8b65-d8f187636a2b

## About the Role

Technical Leadership, Technical Leadership, Artificial Intelligence (AI), Modeling Languages, Cloud Computing, Systems Scalability, Microsoft Windows Azure, Research & Development (R&D), Architectural Services, Mentoring, 6-10 years of experience in AI/ML engineering, data science, or AI platform development.

Hands-on experience with Generative AI and Large Language Models (LLMs) in production

environments.

Strong experience designing RAG architectures, vector search systems, and AI agents.

Experience building AI-powered applications and services using frameworks such as LangChain,

LlamaIndex, Semantic Kernel, or similar.

Experience deploying AI workloads in cloud environments (Azure, AWS, or GCP).

Experience designing scalable distributed systems and microservice-based architectures.

## Description

We are seeking a Senior AI Engineer with 6-10 years of experience in artificial intelligence, machine

learning, and generative AI to lead the design and delivery of enterprise-grade AI solutions. The ideal

candidate will have deep expertise in Large Language Models (LLMs), Retrieval-Augmented Generation

(RAG), and AI agent architectures, along with experience deploying scalable AI systems in cloud-native

environments.

In this role, you will architect, build, and scale advanced AI capabilities using modern AI platforms such

as Azure OpenAI, Anthropic, and other LLM services, enabling innovation across SHS R&D digital

platforms. You will also provide technical leadership, mentorship, and architectural guidance to

engineering teams developing AI-powered products and solutions.

Key Responsibilities

AI Solution Architecture

Design and architect enterprise-grade generative AI solutions, including RAG pipelines,

knowledge retrieval systems, and agent-based AI workflows.

Define scalable AI architecture patterns for integrating LLM capabilities into internal platforms

and applications.

Lead the design of AI-driven digital solutions that align with enterprise architecture, security,

and compliance requirements.

LLM & Generative AI Engineering

Lead the development and optimization of LLM-powered applications using technologies such as

Azure OpenAI, Anthropic Claude, and other foundation models.

Architect and implement RAG pipelines, semantic search, vector databases, and knowledgegrounded

AI systems.

Design AI agent orchestration workflows for complex enterprise automation scenarios.

Platform Engineering & Scalability

Build scalable, production-grade AI systems with robust monitoring, evaluation, and

performance optimization.

Implement MLOps/LLMOps practices, including model lifecycle management, prompt

evaluation, observability, and CI/CD integration.

Optimize AI systems for latency, cost, and reliability in production environments.

Technical Leadership

Provide technical leadership and architectural direction for AI initiatives across multiple teams.

Mentor junior AI engineers and data scientists in best practices for LLM engineering, prompt

engineering, and AI system design.

Conduct technical reviews, architecture validations, and design consultations for AI initiatives.

Research & Innovation

Continuously evaluate emerging advancements in Generative AI, NLP, multi-agent systems, and

foundation models.

Drive innovation initiatives, prototypes, and proof-of-concepts to explore new AI capabilities

relevant to R&D workflows.

Identify opportunities to leverage AI for productivity, automation, and knowledge management