Senior AI Engineer

Job Title: Senior AI Engineer

Location: Atlanta GA

Work Mode: 5 days onsite (Let me know if anybody asks for 3-4 days)

JD:-

Senior AI Engineer — Conversational & Agentic AI

Job Description

Experience

10+ years overall, 3-4+ in GenAI/Agentic AI

Focus

Build conversational AI based product on AWS

Location

Atlanta, GA (USA)

About the role

You will work closely with a client team in Atlanta to build a conversational AI product on AWS in a fast-paced environment. You own major components end to end, turn the architecture into production-quality code, and help the team move from concept to a production-ready release on an accelerated timeline. You are comfortable demoing to client stakeholders and leading technical work for less experienced engineers.

What you will do

Build the core of the product — conversational AI, RAG and agents on AWS Bedrock/Bedrock AgentCore — working closely with the AI Architect.

Implement chatbot features: multi-turn conversation flows, session memory, tool calling, streaming responses and human handoff.

Build RAG pipelines: document ingestion, chunking, embeddings, hybrid search, reranking, metadata filtering and source citation.

Develop MCP servers that expose enterprise APIs and data as governed tools, and integrate agents with each other using A2A.

Drive rapid, iterative delivery: ship a working MVP quickly, then harden, optimise and load test it for production.

Engineer for scale: tune latency, throughput and cost so the chatbot holds up under thousands of concurrent users.

Build evaluation suites for answer quality, groundedness and regression testing of prompts and models.

Ship through CI/CD with infrastructure as code, logging, tracing, alerting and cost monitoring.

Work as part of the client team: estimate and break down work, join weekly demos and explain technical trade-offs to stakeholders.

Review code, mentor engineers and contribute to runbooks and technical documentation for handover.

Must have

Enterprise delivery: built and shipped at least two production-grade GenAI or conversational AI applications, with ownership of significant components, including one delivered on a tight timeline.

Embedded, fast-paced delivery: worked as part of consulting or client teams; comfortable with ambiguous scope, weekly demos and stakeholder exposure.

Hands-on engineering: strong production Python and REST APIs

Conversational AI: built production chatbots or virtual assistants with multi-turn context, intent handling, session memory, human handoff and multichannel delivery (web, mobile)

Chatbot scale: worked on chatbots running at high concurrency in production. Candidates should state peak concurrent users, p95 latency and their part in scaling it.

Scale engineering: response streaming, semantic and response caching, retries and rate-limit handling, provisioned throughput, autoscaling and load testing.

RAG: implemented retrieval pipelines with vector stores (OpenSearch, pgvector, Aurora, Pinecone or similar) and measured retrieval quality and groundedness.

AWS Bedrock (essential): model invocation and selection, Knowledge Bases, Guardrails, Agents and model evaluation.

Bedrock AgentCore (essential): hands-on with Runtime, Memory, Gateway, Identity and Observability to deploy and operate agents.

MCP: built MCP servers and clients, including authentication and authorisation for tools.

A2A and multi-agent: built multi-agent workflows using A2A and frameworks such as Strands Agents, LangGraph or CrewAI.

AWS foundations: Lambda, API Gateway, ECS or EKS, DynamoDB, S3, IAM, VPC networking and CloudWatch.

Responsible AI: guardrails, hallucination control, prompt-injection defence and PII handling 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.

Nice to have

Voice AI with Amazon Connect, Lex or speech models.

Equivalent platforms on Azure OpenAI, Vertex AI or Databricks Mosaic AI.

Fine-tuning, distillation or small-model deployment for cost and latency.

Product mindset: conversational UX, user feedback loops and A/B testing of prompts or flows.

Front-end chat UI experience (React).

AWS Certification on AI/GenAI

Domain experience in Finance