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# GEN AI Engineer

- **Company:** Tata Consultancy Services Limited
- **Location:** Tampa, FL, United States
- **Salary:** $100,000.0 - $130,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Databases, Python (Programming Language), Natural Language Processing, Search Technologies, TypeScript, Large Language Models, Multi-Agent Systems, Prompt Engineering, Deep Learning, Model Validation, Generative AI, Virtual Agents, GPT, Microservices
- **Published:** May 19, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=f0c678b7b447c879

## About the Role

Technical Skills
Strong experience with Python (mandatory); exposure to Java/TypeScript is a plus.
Hands-on experience with LLMs and GenAI frameworks (e.g., LangChain-style orchestration, agent frameworks).
Experience with Agentic AI concepts: planning, tool use, memory, multi-agent coordination.
Experience with vector databases and semantic search.
API integration and microservices architecture.
Cloud platforms (Azure / AWS / GCP).
AI/ML Knowledge
Understanding of NLP, deep learning, and transformer-based models.
Experience with model evaluation, hallucination mitigation, and prompt optimization.
Familiarity with Responsible AI, security, and data privacy principles.

## Description

Design and implement GenAI solutions using LLMs for text, code, and knowledge-based use cases.
Develop prompt engineering, prompt orchestration, and reusable prompt libraries.
Fine-tune and evaluate LLMs for enterprise-specific tasks.
Integrate GenAI capabilities into applications, APIs, and workflows.
Agentic AI & Autonomous Systems
Design and implement agentic architectures (planner, executor, evaluator, memory).
Build multi-agent systems with task decomposition, tool usage, and feedback loops.
Enable agents to interact with enterprise tools, APIs, databases, and knowledge stores.
Implement agent memory, reasoning chains, and self-reflection mechanisms.
Architecture & Engineering
Define end-to-end AI solution architecture (cloud, APIs, security, observability).
Implement Retrieval-Augmented Generation (RAG) with vector databases.
Ensure scalability, performance, reliability, and cost efficiency.
Apply Responsible AI, governance, and compliance standards.
Collaboration & Delivery
Work closely with product owners, domain experts, and engineering teams.
Translate business problems into AI-driven solutions.


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