[Hiring] AI Technical Lead @NTT DATA, Europe & LATAM, Branch in USA, Inc.

Role Description

NTT DATA is seeking an AI Technical Lead, Amazon Bedrock to design and implement AI-agent capabilities for Customer Service and Contact Center use cases as part of a broader Amazon Connect transformation program. This hands-on technical leadership role will span:

• Generative AI architecture
• Agentic workflows
• RAG
• Prompt and context engineering
• Model evaluation
• Guardrails
• Enterprise integrations

The ideal candidate combines strong software engineering with practical LLM expertise, can determine when deterministic software, retrieval, workflows, or LLM reasoning are most appropriate, and can take solutions from prototype through safe, production-ready deployment.

Responsibilities include:

• Design and implement Generative AI and AI-agent solutions for Customer Service and Contact Center scenarios.
• Design prompts, system instructions, context strategies, tool-calling patterns, and agent workflows.
• Design and implement Retrieval-Augmented Generation (RAG) solutions using enterprise knowledge sources.
• Integrate AI agents with APIs, databases, business applications, knowledge repositories, and external tools.
• Evaluate and select appropriate foundation models for different use cases, balancing quality, latency, cost, and operational constraints.
• Design mechanisms to improve grounding, reduce hallucinations, and manage unsupported or out-of-scope requests.
• Implement guardrails, content controls, PII protection, and safety mechanisms appropriate for enterprise use.
• Design conversational context, session state, memory, fallback, and human-escalation patterns.
• Define observability, tracing, quality metrics, and operational monitoring for AI-agent behavior.
• Optimize prompts, retrieval strategies, model configuration, token consumption, latency, and cost.
• Work with AI Output Testing Engineers to create evaluation datasets, regression suites, and release quality thresholds.
• Support AI governance and Responsible AI requirements by translating policy expectations into technical controls.
• Participate in architecture reviews, code reviews, technical documentation, and production support.

Qualifications

• 10+ years of relevant technical experience including delivering Generative AI / LLM-based applications in enterprise environments.
• 2+ years of experience with Amazon Bedrock.
• Strong software engineering experience, preferably using Python and/or TypeScript.
• Practical experience with prompt engineering, context engineering, RAG, embeddings, vector search, semantic search, and tool/function calling.
• Experience designing or implementing AI agents and multi-step agentic workflows.
• Experience integrating LLM solutions with REST APIs, enterprise applications, databases, and knowledge sources.
• Strong understanding of hallucination mitigation, grounding, prompt injection, jailbreak prevention, content filtering, and context-window management.
• Experience evaluating model and agent outputs using automated and human evaluation approaches.
• Experience with Git, CI/CD, automated testing, and production software delivery practices.
• Ability to communicate complex AI concepts clearly to architects, developers, product teams, and business stakeholders.
• Professional proficiency in English.

Nice-to-Have

• Previous experience integrating Generative AI capabilities with Amazon Connect or other enterprise Contact Center platforms.
• Experience with Amazon Q in Connect, agent-assist, knowledge-assistance, or customer-support AI use cases.
• Experience with frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, or equivalent agent orchestration frameworks.

Education & Certifications

• Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a related technical field.

Required Equipment

This role requires the use of a personal laptop. Candidates must be comfortable with company security software and device management tools being installed on their laptop as part of the onboarding process and for the duration of the engagement.