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# Senior Technical Lead

- **Company:** HCL America Inc.
- **Location:** United States
- **Experience:** Expert
- **Salary:** $151,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Large Language Models, Generative AI, Service Stack
- **Published:** August 5, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=1b559c5c6192852d

## About the Role

Solid engineering fundamentals across legacy and modern technology stacks.

AI fundamentals, including:
Prompting
Context Handling
Retrieval-Augmented Generation (RAG)
AI Evaluations

Experience using AI coding tools.
Ability to build working prototypes rapidly.

Understanding of AI/LLM techniques, including:
Prompting
RAG
Agent Design
Evaluations

Experience developing deterministic scripts and tooling.
Ability to design solutions within security, compliance, and data-access constraints.

Experience building:
Scripts
Validation Checks
Guardrails
Evaluations

Other Requirements

6-8 years of experience combining hands-on delivery and architecture ownership.
Legacy modernization experience.
Client-facing experience, including proposals, demos, and workshops.
Experience owning at least one project end-to-end.
Ability to work without detailed specifications.
Ability to build trust with client teams.
Ability to switch context quickly across workstreams.

## Description

Responsible for working across legacy and modern technology stacks, applying AI fundamentals and AI coding tools to solve client problems. The role involves understanding client constraints, building and validating prototypes, selecting appropriate AI and non-AI approaches, delivering solutions end-to-end, and adapting solutions to different client environments., Work within legacy and modern technology environments.
Apply AI fundamentals including prompting, context handling, RAG trade-offs, and evaluations.
Use AI coding tools to transform ambiguous ideas into working prototypes.
Determine when to use AI/LLM techniques versus deterministic scripts and tooling.
Understand client constraints, including approved tools, data-access boundaries, security requirements, and compliance expectations.
Design solutions that operate within client constraints.
Adapt solutions for different client environments rather than applying a standard template.
Own engagements from problem discovery through prototype development, validation, and handoff.
Build scripts, validation checks, guardrails, and evaluation mechanisms around AI solutions.
Bridge the gap between platform capabilities and client needs.
Feed client-specific learnings back into the core team to improve future engagements.
Participate in proposals, demos, and workshops.


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- [Creating Industry ready solutions with LLM Models](https://www.wearedevelopers.com/videos/899-creating-industry-ready-solutions-with-llm-models)
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- [Introducing a Digital Service Catalog for speed and scale](https://www.wearedevelopers.com/videos/763-introducing-a-digital-service-catalog-for-speed-and-scale)
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- [The shadows that follow the AI generative models](https://www.wearedevelopers.com/videos/624-the-shadows-that-follow-the-ai-generative-models)

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