Lead Software Engineer

• You build it, you run it. End-to-end accountability for the AI platform in production: architecture, delivery, operations, cost, and quality, with on-call for what you ship. No hand-off to a separate ops or QA function.
• Design, build, and operate agentic AI systems: LLM services, retrieval pipelines, multi-agent orchestration, agent execution runtimes, and human-in-the-loop capabilities.
• Build and operate memory infrastructure for agent systems (storage, retrieval, promotion/demotion mechanics). Applied Science owns promotion criteria and scientific soundness.
• Own LLMOps: the harnesses, pipelines, and infrastructure that keep non-deterministic systems reliable and correct in production, including dynamic model selection by task, cost, and latency.
• Build automated eval infrastructure that continuously compares offline and online metrics and alerts on drift or regression. Applied Science owns the methodology and acceptance thresholds it checks against.
• Evaluate, adopt, and integrate third-party LLMOps and eval platforms where they accelerate delivery over building in-house.
• Build the technical controls, telemetry, and guardrails that enforce compliance frameworks (e.g., ISO 42001, AIUC-1) in the architecture itself.
• Build and maintain the agent definition management APIs and the tool registry definitions reference, keeping definitions stable as tools and models version independently.
• Lead the engineering build of proof-of-concepts. Applied Science defines the architecture bet and validation criteria being tested.

Core Responsibilities

• Create architecture, prototypes, and design proposals that deliver near-term business value while aligning with the future-state product and platform vision.
• Influence technical direction through architecture reviews, RFCs, design discussions, and hands‑on collaboration.
• Partner with senior developers, architects, and tech leads to define and execute the product/platform architecture roadmap.
• Balance delivery speed, quality, maintainability, and long-term platform health when making technical decisions.
• Break down large initiatives into parallelizable chunks of work that deliver incremental business value.
• Champion proposals through all phases of the SDLC, from design through production implementation.
• Contribute to improving the core product build, CI/CD pipelines, and overall SDLC.
• Troubleshoot and eliminate root causes of persistent production issues; drive reliability, observability, monitoring, and incident response.
• Keep technical documentation current and create new artifacts as needed.
• Facilitate design discussions within and across teams.
• Represent the team in technical discussions with Tech Leads, Operations, Product/UX, Security, Domain SMEs, and external stakeholders.
• Act as a trusted technical leader, helping teams succeed through collaboration, influence, and shared ownership.
• Mentor other developers through design reviews, joint agentic-AI sessions, and feedback on skills, prompts, and other agent inputs; contribute to raising overall engineering maturity.
• Share knowledge of new technologies and industry best practices.
• Evaluate emerging technologies, frameworks, and cloud capabilities that could benefit the platform.

AI-Augmented Development Expectations:

• Use AI‑assisted development tools — inline completion, conversational assistants, and agentic workflows — as a standard part of your daily workflow.
• Critically evaluate and take ownership of all AI‑generated output before it ships — verify correctness, security, and alignment with architecture and team standards.
• Identify opportunities for agentic automation within your team, and lead the rollout of new AI‑assisted workflows and tooling.
• Mentor other developers on effective, responsible use of AI‑assisted development tools.
• Help establish team‑level practices and guardrails for using AI tools safely and effectively.
• Build and refine feedback loops that improve the team's collective use of AI tooling over time.

What You Will Bring:

• 2+ years building and operating production AI systems, with a working grasp of the latency, cost, accuracy, and reliability trade‑offs involved.
• Experience with agent frameworks, agent memory systems, or orchestration of tool‑using AI systems.
• Production AWS experience, including Infrastructure as Code (CDK, CloudFormation, or Terraform).
• Degree in Computer Science, Software Engineering, or equivalent practical experience; 8+ years of professional software development experience with demonstrated impact beyond a single team.
• Proven experience designing and operating distributed, cloud‑native, SaaS/multi‑tenant platforms at scale, ideally on AWS.
• Strong