Senior Software Developer III*

What You’ll Be Doing:

• 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).