Sr AI Developer

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.