Senior AI Engineer

Cyberobotix LLC; Atlanta (GA); Presencial; USD 180.000 - 260.000; Jornada completa

Descripción de la vacante

Cyberobotix LLC in Atlanta, GA seeks a Senior AI Engineer — Conversational & Agentic AI to own major components end-to-end, turning architecture into production-quality code and delivering on accelerated timelines within a client-focused team.

You will build conversational AI on AWS Bedrock, implement RAG pipelines, MCP servers, and scale for thousands of concurrent users in a fast-paced consulting environment.

Formación

• Enterprise delivery: shipped two production-grade GenAI apps with ownership of major components.
• Embedded, fast-paced delivery in consulting/client teams; ambiguous scope and weekly demos.
• Hands-on engineering: strong production Python and REST APIs.
• Conversational AI: production chatbots with multi-turn context, memory and handoff.

Responsabilidades

• Build the core product — conversational AI, RAG and agents on AWS Bedrock.
• Implement chatbot features: multi-turn flows, session memory, tool calling, streaming responses and human handoff.
• Build RAG pipelines: ingestion, chunking, embeddings, hybrid search, reranking, metadata filtering and source citation.
• Develop MCP servers exposing enterprise APIs and data as governed tools; integrate agents with A2A.
• Deliver MVP quickly; harden, optimize and load test for production.
• Engineer for scale: tune latency, throughput and cost for thousands of concurrent users.
• Build evaluation suites for answer quality, groundedness and prompt/model regression testing.
• CI/CD with IaC, logging, tracing, alerting and cost monitoring.
• Work with client team: estimate work, join weekly demos and explain trade-offs to stakeholders.
• Review code, mentor engineers and contribute to runbooks and handover docs.

Conocimientos

Python

REST APIs

GenAI/Agentic AI

AWS Bedrock

MCP servers

A2A multi-agent

LLM observability

Herramientas

Bedrock AgentCore

OpenSearch

Langfuse

Descripción del empleo

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.