Day-to-Day Responsibilities:

Agentic AI Platform Development

Design, build, and maintain production-grade agentic AI workflows using AWS Bedrock and modern AI frameworks.
Develop AI systems that combine enterprise knowledge, engineering documentation, and internal data sources to support hardware engineering workflows.
Implement multi-step reasoning, tool usage, and autonomous task execution within agentic workflows.
Design and optimize prompts, agent architectures, and evaluation frameworks to maximize AI performance and accuracy.

RAG & Knowledge Systems

Build and maintain Retrieval-Augmented Generation (RAG) solutions using embeddings, vector databases, and knowledge bases.
Develop data ingestion, transformation, and indexing pipelines for engineering documentation, design artifacts, and technical datasets.
Design retrieval strategies across structured and unstructured data sources, including graph and search-based retrieval systems.
Continuously improve retrieval quality, ranking, and response accuracy.

Engineering Productivity & Stakeholder Engagement

Partner with hardware engineers, data owners, and cross-functional stakeholders to identify productivity opportunities and prioritize features.
Gather feedback from engineering users and incorporate findings into future platform enhancements.
Help define product direction, roadmap priorities, and long-term architecture for the AI platform.
Serve as a technical leader, helping break down complex initiatives into executable workstreams.

Platform Reliability, Security & Operations

Implement Human-in-the-Loop (HIL) workflows, validation mechanisms, and governance controls.
Design secure AI systems using enterprise security best practices, including IAM, encryption, audit logging, and data protection controls.
Build scalable backend services, APIs, and microservices supporting AI workflows.
Debug, test, monitor, and optimize deployed AI solutions for performance, reliability, and cost efficiency.
Support CI/CD pipelines and production deployment processes for AI applications.

Required Qualifications:

Core AI & Agentic Development Experience

Proven experience building and deploying production-grade agentic AI systems.
Strong hands-on experience with AI workflow development using AWS Bedrock or comparable cloud AI platforms.
Experience building Retrieval-Augmented Generation (RAG) systems using embeddings, vector databases, and enterprise knowledge sources.
Experience implementing agentic workflows, multi-step reasoning, tool usage, and autonomous task execution.
Familiarity with Model Context Protocol (MCP) and integrating external tools and data sources into AI workflows.
Experience designing evaluation frameworks and validation mechanisms for AI systems.

Software Engineering & Backend Development

Strong Python development skills.
Experience building backend services, APIs, and microservices.
Experience with Flask or similar Python web frameworks.
Strong understanding of software engineering best practices, testing, debugging, and code quality.
Experience working with CI/CD pipelines and modern software delivery processes.

Cloud & Data Engineering

Experience with cloud-native application development in AWS or similar cloud environments.
Experience building data ingestion, transformation, and indexing pipelines.
Knowledge of AWS services including: Bedrock, Lambda, Step Functions, EventBridge, S3, DynamoDB, Kendra, Neptune (or similar graph databases)
Experience designing graph-enhanced or hybrid RAG architectures.

Security & Governance

Experience implementing Human-in-the-Loop (HIL) workflows and AI governance controls.
Understanding of secure AI system design, including:
- IAM and least-privilege access
- Encryption and key management
- Audit logging
- Data protection and PII handling
Experience designing reliable distributed systems and asynchronous workflow orchestration.

Candidate Profile

Demonstrated hands-on experience using agentic AI tools in real-world development environments.
Strong understanding of how to maximize AI effectiveness through prompt engineering, workflow design, and agent orchestration.
Ability to work independently, define technical direction, and collaborate effectively with cross-functional stakeholders.
Seniority measured by depth of relevant AI and agentic development experience rather than total years of software engineering experience.

Nice-to-Haves:

Experience with LangChain, LangGraph, LlamaIndex, Hugging Face, or similar AI frameworks.
Experience building agentic reasoning loops with tool usage through MCP.
Experience implementing HIL clarification workflows and human approval checkpoints.
Infrastructure-as-Code experience using AWS CDK (TypeScript preferred).
Familiarity with model governance frameworks, content classification, and AI safety controls.
Experience with dependency scanning, vulnerability management, and security gates in CI/CD pipelines.
Experience working in monorepo or multi-package development environments.
Background supporting engineering productivity tools, developer platforms, or internal engineering enablement initiatives.
Experience supporting hardware engineering, CAD systems, or engineering knowledge management platforms.
Data science or machine learning background with experience evaluating model performance and accuracy.

Learn more about this Employer on their Career Site

Sr AI Developer