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
5 10 years overall, 3+ 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
...Passion, Integrity, and Work Ethic in every interaction with guests and team members. Collect laundry from the entire property for cleaning. Sort and separates laundry by category for assembly. Dump linen from the carts to the belt, and keep up with the pace with...
...continual training and performance feedback that even experienced teachers appreciate. Become a more effective teacher as you enhance your... ...ready for you. Year-round flexible hours: Our after-school and summer hours adapt to your changing schedule. Since no preparation is...
...per year Requirements: ~8+ years of experience in product management or a closely related domain ~ Advanced knowledge of the product... ...Morgan on this opportunity for a Product Manager in Kinexys Digital Assets. We are a global financial institution with over 200 years of...
...and lead delivery teams in an Agile environment. Work as a Scrum Master in a SAFe Agile environment. Support Marketing Technology... ...digital product organizations. Required Skills: 5+ years of experience as a Scrum Master role in an Agile environment. Strong...
Performs all duties to maintaining general cleanliness and safety on the floor, in seating areas, restrooms, and back offices. All Whole Foods Market Retail jobs require ensuring a positive company image by providing courteous, friendly, and efficient service to customers...