Senior Software Engineer (Agentic Platform)

Senior Software Engineer (Agentic Platform)

Reports to: Co-Founder / CPO
Day-to-day Partner: Lead Architect
Department: Product & Engineering
Location: Remote (US)
Company Stage: Venture-backed AI company (Seed to Series A)

Role Overview

A venture-backed AI company building infrastructure and software that helps highly regulated organizations operationalize compliance, governance, and trustworthy AI is seeking a Senior Software Engineer to join its platform team.

This is a hands-on engineering role focused on practical AI and agentic prototyping, shipping platform capabilities, exploring emerging AI patterns, and helping turn promising experiments into reliable product features.

In the first months, you will work within the architectural direction set by the Lead Architect, contribute production code from day one, and build context across the platform, AI initiatives, and regulated-industry use cases. You will prototype and help implement patterns such as multi-agent workflows, retrieval-augmented systems, vector databases, knowledge graphs, and AI-assisted SDLC workflows, while applying disciplined engineering judgment to keep the work secure, testable, maintainable, and product aligned.

Success comes from strong software engineering fundamentals, fast learning, thoughtful experimentation, and the ability to move from prototype to production-quality implementation under clear architectural guidance.

Scope of the Role & Core Responsibilities

• Platform Engineering: Build and maintain core platform services and agentic product capabilities.
• AI-Augmented Engineering: Use AI tools across the SDLC and apply engineering judgment to validate outputs.
• Agentic Platform: Explore, prototype, and help implement practical patterns for multi-agent workflows, retrieval, vector / graph systems, and AI-assisted SDLC capabilities.
• Collaboration, Quality, and Growth: Work with platform teammates, domain experts, and distributed engineering pods as a peer contributor and reviewer while growing into broader AI platform ownership over time.

Core Responsibilities

Platform Engineering

• Build and evolve core platform services using Python, Kubernetes, and event-driven cloud-native architectures on Azure.
• Implement production-grade backend services that meet standards for reliability, security, scalability, observability, and maintainability.
• Contribute to features supporting traceability, validation, auditability, and evidence generation for regulated customers.
• Follow architectural direction set by the Lead Architect, raising questions and proposing improvements as understanding grows.

AI-Augmented Engineering

• Work within an AI-augmented engineering workflow using tools such as Claude Code, GitHub Copilot, Cursor, or similar systems for development, testing, debugging, refactoring, documentation, and review.
• Apply engineering judgment to validate AI-generated outputs for correctness, security, maintainability, and architectural fit.
• Learn and apply emerging AI engineering patterns including multi-agent systems, retrieval-augmented systems, vector databases, and knowledge graphs.
• Contribute to evaluation, monitoring, and guardrail practices for AI-enabled features.

Agentic Platform

• Explore and prototype emerging AI engineering patterns, including multi-agent systems, autonomous workflows, agent orchestration, AI-assisted SDLC workflows, agentic entitlements, retrieval-augmented systems, vector databases, knowledge graphs, and graph-based reasoning.
• Help translate promising AI / agentic prototypes into testable, maintainable platform components that can support real customer workflows.
• Document lightweight design notes, spike outcomes, test results, and tradeoffs so the team can decide what to productize, defer, or discard.

Collaboration, Quality, and Growth

• Collaborate with the Lead Architect, product leadership, domain experts, and distributed engineering pods as a peer engineer on shared deliverables.
• Provide code review and quality feedback within assigned scope, especially for AI-assisted code, agentic features, and prototype-to-product transitions.
• Surface delivery risks, quality gaps, security concerns, unclear requirements, or technical ambiguity early to the Lead Architect and relevant stakeholders.
• Grow into expanded AI platform, architecture, and technical leadership scope over time as judgment and context develop.

What Success Looks Like in 6 Months

• Confidently navigates and modifies the platform's microservices.
• Ships production features independently within the architectural direction set by the Lead Architect.
• Demonstrates working knowledge of the platform and contributes meaningfully across multiple services or AI-enabled capabilities.
• Produces practical agentic / RAG / vector / graph prototypes that clarify product direction and can be evaluated against real use cases.
• Helps turn selected experiments into reliable, testable, maintainable platform components.
• Becomes a trusted contributor and second reviewer on AI-assisted engineering work, agentic platform features, and relevant vendor PRs.
• Demonstrates growth in agentic AI patterns and regulated-industry delivery practices.

Who We're Looking For

You are an experienced engineer who values building, learning, and experimentation. You are comfortable working in a hands-on role where emerging AI ideas need to be explored, tested, simplified, and hardened before they become product capabilities.

You bring strong fundamentals, humility, curiosity, and the maturity to separate demo-worthy experiments from production-ready engineering. You do not need to be a researcher or a principal architect. You do need to be a strong software engineer who is already using AI-assisted development tools, is curious about agentic systems, and can apply disciplined judgment in a high-trust product environment.

Core Qualifications

• 8+ years total experience, with 5+ years building production cloud-native services in Python on Azure, and hands-on use of AI-assisted development tools.
• Expert proficiency in Python and working knowledge of at least one additional language.
• Strong cloud-native development experience: APIs, container-based architectures, observability, and CI/CD (Azure preferred).
• Comfortable deploying and operating application services on Kubernetes.
• Fluent with AI-assisted development tools and able to critically evaluate their outputs.
• Strong software quality practices: automated testing, code review, secure development, maintainability, and production readiness.
• Familiarity with DevSecOps and secure SDLC practices.
• Relational database design; familiarity with vector, graph, or other modern data approaches is a plus.
• Strong written and verbal communication, especially for technical documentation, spike notes, design tradeoffs, and handoffs.
• Comfortable in ambiguous, fast-moving environments and excited to learn quickly inside an AI startup.

Helpful Experience

• Exposure to multi-agent systems, agent orchestration, AI-assisted SDLC workflows, retrieval-augmented generation, vector databases, knowledge graphs, or graph-based reasoning.
• Experience prototyping new technical approaches and helping mature selected prototypes into production features.
• Experience in regulated industries such as pharma, life sciences, healthcare, finance, government, aerospace, or other high-trust environments.
• Experience working with distributed engineering teams or contractor pods as a peer contributor.
• Azure, Azure DevOps, Kubernetes, event-driven architectures, infrastructure-as-code, and production observability.
• Exposure to Responsible AI, AI governance, compliance automation, or evidence-generation systems.