Lead AI Engineer - Atlanta, GA

DTS is looking for experienced Lead AI Engineer for our Direct Client position based in Atlanta, GA Job Description: You will work closely with a client team in Atlanta to design and build a conversational AI product on AWS in a fast-paced environment. You will lead requirement gathering with business and technical stakeholders, own the end-to-end architecture, make fast technical calls, and stay hands-on so the team moves from concept to a production-ready release on an accelerated timeline. You are equally comfortable at a whiteboard with client leadership and in a code review with engineers. What you will do Lead requirement gathering: run discovery workshops with business and technical stakeholders, map user journeys and conversation flows, capture functional and non-functional requirements, and turn them into a prioritised backlog with clear acceptance criteria. Define the target architecture for the product early and decisively - conversational AI, RAG and agents on AWS Bedrock/Bedrock AgentCore. Design chatbots and assistants that can serve thousands of concurrent users with predictable latency, cost and uptime. Architect agent ecosystems using MCP for tool and data access and A2A for agent-to-agent collaboration. Drive rapid, iterative delivery: get to a working MVP quickly, then harden, load test and take the product to production readiness. Build hands-on alongside the team: agent code, prompts, retrieval pipelines, integrations with data platforms and enterprise systems, and infrastructure as code. Set standards for LLMOps: evaluation, prompt and model versioning, observability, guardrails and cost governance. Own non-functional design: security, identity and access, PII handling, compliance, resilience and disaster recovery. Work as part of the client team: shape scope and trade-offs with stakeholders, run weekly demos and present architecture and progress to client leadership. Run design reviews, mentor engineers and hand over a documented, operable platform (runbooks, architecture decisions, cost model) at the end of the engagement. Must have Enterprise delivery: architected and delivered multiple production-grade GenAI or conversational AI products end to end, including at least one taken from concept to production on a tight timeline. Embedded, fast-paced delivery: worked as part of consulting or client teams; comfortable with ambiguous scope, weekly demos and senior stakeholder exposure. Requirements and discovery: led discovery and requirement workshops for AI products; able to translate business goals into use cases, conversation flows, user stories and measurable success criteria. Hands-on builder: writes production Python and infrastructure as code (CDK, Terraform or CloudFormation) - not a diagram-only architect. Conversational AI: production chatbots or virtual assistants with multi-turn context, intent handling, session memory, human handoff and multichannel delivery (web, mobile, messaging, contact centre). Chatbot scale: designed systems running at high concurrency in production. Candidates should state peak concurrent users, p95 latency and daily conversation volume they handled. Scale engineering: response streaming, provisioned throughput and quota planning, semantic and response caching, load testing, autoscaling and graceful degradation under model rate limits. RAG: retrieval pipeline design - chunking, embeddings, hybrid search, reranking, metadata filtering, vector stores (OpenSearch, pgvector, Aurora, Pinecone or similar) and groundedness evaluation. AWS Bedrock (essential): foundation model selection, Knowledge Bases, Guardrails, Agents, model evaluation and cost optimisation. Bedrock AgentCore (essential): Runtime, Memory, Gateway, Identity and Observability for deploying and operating agents securely at scale. MCP: designed MCP servers and clients that expose enterprise APIs and data as governed tools, including authentication and authorisation. A2A and multi-agent: orchestration patterns (supervisor, hierarchical, peer-to-peer) using A2A and frameworks such as Strands Agents, LangGraph or CrewAI. Foundations: strong AWS architecture (serverless, containers, networking, IAM, security), distributed systems and API design; Python hands-on. Responsible AI: guardrails, hallucination control, prompt-injection defence, auditability and data privacy in regulated environments. LLM observability and evaluation: Langfuse, Ragas, Bedrock evaluations or similar tools to trace, monitor and evaluate LLM applications. Location: based in or able to relocate to Atlanta; US work authorisation required.