Directly focused on building agentic AI systems using Bedrock, LangChain/LangGraph and rapid prototyping; heavy on AI orchestration and productionizing agents.
About the Role
Build, deploy, integrate, and operate enterprise-grade agentic AI solutions on AWS, working directly with business and engineering stakeholders to convert high-value use cases into reliable production outcomes. The role combines platform ownership, AI orchestration, cloud operations, and forward-deployed engineering with end-to-end production accountability.
Job Description
Role
The Agentic AI & Forward Deployed Engineer is responsible for translating business and operational needs into secure, scalable, and supportable agentic AI capabilities. This hands-on, client-facing role covers the full lifecycle from discovery and rapid prototyping to AWS deployment, integration, production stabilization, release management, and ongoing operational ownership.
Key Responsibilities
- Partner with business users, product owners, architects, data, security, and cloud teams to convert ambiguous requirements into executable designs, prototypes, and production increments.
- Rapidly prototype single-agent and multi-agent workflows, validate feasibility with enterprise data/systems, and iterate on feedback.
- Design and implement agent orchestration (including tool boundaries, control flow, handoffs, termination) using AWS Bedrock Agents and frameworks such as LangGraph or LangChain.
- Implement tool calling for enterprise APIs, databases, search systems and manage input validation and permission controls.
- Design Retrieval-Augmented Generation (RAG) pipelines, ingestion, chunking, retrieval, grounding, citation handling, and content refresh processes.
- Implement short-term and long-term memory/context layers while managing privacy, token usage, retention, and cross-session boundaries.
- Build full-stack components: React + TypeScript frontends and Python backends (FastAPI where suitable), plus REST APIs, microservices, and workflow orchestration.
- Engineer and operate AWS infrastructure (Bedrock, DynamoDB, S3, Lambda, ECS/Fargate, API Gateway, IAM, CloudWatch, EventBridge, Secrets Manager) and contribute IaC (Terraform where used).
- Integrate with data platforms (Databricks, SQL, enterprise pipelines) and support Dataiku patterns when applicable.
- Use GitHub and CI/CD for source control, validations, artifact management, and controlled deployments across Dev/QA/UAT/Prod.
- Own production support: monitoring, incident triage, troubleshooting, root-cause analysis, runbooks, documentation, and stakeholder communications.
Requirements
- Bachelor’s degree in Computer Science, Engineering, IT, or equivalent practical experience.
- Strong hands-on experience delivering cloud-native applications and services on AWS in enterprise environments.
- Demonstrated experience building LLM or agentic AI solutions involving orchestration, tool calling, RAG, and context/memory management.
- Proficiency in Python and working knowledge of React and TypeScript for full-stack delivery; experience with REST APIs, microservices, and event-driven patterns.
- Experience supporting production systems, managing incidents and defects, and promoting releases across controlled environments.
- Ability to engage directly with users and stakeholders, structure ambiguous problems, prioritize work, and communicate decisions clearly.
- Strong documentation habits and willingness to own solutions beyond initial development.
Preferred Qualifications
- Experience with AWS Bedrock Agents, LangGraph, LangChain, MCP, or comparable agent orchestration technologies.
- Experience integrating Databricks and/or Dataiku with enterprise applications and AI workflows.
- Working knowledge of Terraform, containerized deployment on ECS/Fargate, and mature CI/CD practices.
- Experience in regulated or highly governed enterprise environments with access, audit, validation, and release evidence requirements.
- Background in consulting, field engineering, solution architecture, or customer engineering.
Platform & Tools
The role centers on AWS-based agentic AI platforms and enterprise integrations, with heavy emphasis on observability, security controls, and production governance.