About the Opportunity Flatiron School is offering an apprenticeship opportunity for early-career developers to earn income while learning. Apprentices will enroll in Flatiron’s AI part-time bootcamp and be matched with one of Bletchley Fellowship’s employer partners for a paid apprenticeship throughout the duration of the program. This is a unique opportunity to develop skills in AI and data science while gaining hands-on industry experience. As an apprentice, you’ll contribute to software products, collaborate with experienced engineers, and build your portfolio with production-ready work. At the same time, you’ll deepen your understanding of data and machine learning to become a more versatile, full-stack software developer. This is a full-time opportunity, 40 hours/week (20 hours coursework and 20 hours apprentice work) for 14 months. The apprenticeship earnings will more than cover the tuition cost of the bootcamp. What You'll Do - Attend and complete the Flatiron School Data Science & AI Bootcamp as part of the apprenticeship - Contribute to software projects for your host company - Write clean, maintainable and testable code using modern frameworks and tools - Collaborate in sprint-based workflows and participate in code reviews - Support feature development, debugging, documentation, and QA processes - Build your portfolio of production-level work while growing your technical and collaborative skills - Receive ongoing mentorship from senior developers and product leaders What You'll Learn - How engineering and data science intersect in modern AI products - Practical agile development workflows in a collaborative environment - Best practices for writing clean, maintainable, production-quality code - How to build and ship features with real user needs and product strategy in mind - How to apply data thinking to software engineering challenges Who This Program Is For Requirements: - Must be available 40 hours/week for 14 months to complete the Flatiron School Data Science & AI bootcamp and for apprenticeship work - Have a degree in Computer Science (or similar technical degree) OR have recently completed a software engineering bootcamp (strongly preferred); self-taught developers are also encouraged to apply - Programming proficiency in at least one modern language (e.g., JavaScript, Python, Java) - Familiarity with frontend and backend web development concepts - Working knowledge of databases (SQL or NoSQL) and how to query or integrate them - Must be currently living in the U.S. - Must be authorized to work in the U.S. We do not offer any visa sponsorship at this time - including H-1B, STEM OPT, and F-1 visa transfers. Preferred Skills: - Awareness of testing, debugging, and code review practices - Strong communication and collaboration skills - Accountability in managing time and meeting small milestones - Curiosity, initiative, and a strong desire to grow into a multidisciplinary developer Who This Program Is Not For - Candidates currently enrolled in a college/university or bootcamp program - Candidates currently employed in a full-time job (part-time work is ok, as long as the hours do not conflict with our program) - Candidates who are seeking data analytics or data science apprentice work Program Details - Hourly Pay: $20/hour for 20 hours/week paid through an employer partner (hiring entity) on their internal payroll cadence, 14-month commitment - Tuition Coverage: Commit to pay the full $11,900 bootcamp tuition, through a monthly installment payment plan to Flatiron School - Hours: 40 hours per week total - 20/week in apprenticeship role, 20/week in part-time bootcamp courses - Location: Program is fully remote and online, so you can work and study at home. U.S.-based candidates only Requirements - Availability for 40 hours/week - Successful completion of the Flatiron School Aptitude Assessment - Successfully passing the technical interview with a member of our engineering team

• Complete Flatiron School’s Software Engineering (full-time) and AI & Data Science (part-time) bootcamp programs • Contribute to real software projects during a paid apprenticeship with an employer partner • Collaborate in sprint-based workflows and participate in code reviews • Build a portfolio of production-level work • Receive mentorship from experienced engineers and product leaders

• Design and implement multi-agent architectures using LangGraph • Build stateful, tool-augmented agents capable of reasoning, planning, and execution • Develop agent orchestration patterns (planner-executor, supervisor, hierarchical agents) • Implement memory systems (short-term, long-term, contextual memory) • Enable cross-agent communication and coordination • Build production-grade LLM applications (RAG, agents, copilots) • Integrate tools, APIs, and enterprise systems into agent workflows • Design robust prompting, routing, and guardrails • Optimize workflows for latency, cost, and quality • Develop reusable AI services, agent frameworks, and APIs • Deploy agent-based systems on AWS • Use Docker and Kubernetes for scalable deployments • Implement monitoring and observability (LangSmith) • Evaluate agent performance across reasoning quality and task completion • Improve workflows using feedback loops and tracing • Diagnose and fix failure modes in multi-step pipelines • Work with product and engineering teams to build agentic workflows • Guide teams on best practices for agent design • Contribute to reusable patterns and frameworks

• Design and implement GenAI solutions using AWS Bedrock and Agentcore. • Define architecture for LLM-based applications, including RAG pipelines and agentic workflows. • Develop and orchestrate agentic AI workflows, enabling multi-step reasoning, tool usage, and task automation. • Build and manage RAG pipelines, including embeddings, retrieval mechanisms, and vector databases. • Integrate LLM capabilities into enterprise applications via APIs and backend services. • Design and optimize prompt engineering strategies for accuracy, relevance, and performance. • Work with structured and unstructured data sources to enable knowledge-driven AI applications. • Ensure model evaluation, monitoring, and optimization for latency, cost, and response quality. • Collaborate with application, data, and platform teams for end-to-end solution delivery. • Define best practices for security, governance, and responsible AI usage. • Troubleshoot and resolve issues in production GenAI systems. • Provide technical leadership and mentor team members while remaining hands-on.