Engineer, Agentic Systems

As a Senior Engineer, Agentic Systems, you will play a central role on a small, highly agile engineering team, enjoying high autonomy and ownership of end-to-end product development. This role is well-suited for engineers who thrive in environments with minimal oversight and where shipping real systems rapidly-from asking the right questions to delivering production-ready code-is key.

Core responsibilities include:

• Product Engineering: Collaborate directly with stakeholders to understand vague requests, deduce real needs, and design & ship robust systems without waiting for fully-specified product specs.
• Agentic Systems Engineering: Build, deploy, and productionize LLM-driven workflows, continuously monitoring and hardening them for real-world use.
• System Design: Architect and develop across the stack-front end, back end, real-time/streaming components, databases, and APIs with an eye on scalability, reliability, and maintainability.
• AI-Augmented Engineering: Leverage modern AI coding workflows and coding assistants (e.g., Claude Code, Cursor, Copilot) to maximize efficiency and output, while maintaining critical review and quality checks.
• Agentic LLM System Design: Apply concepts such as agent patterns, tool/function calling, Retrieval-Augmented Generation (RAG), prompt engineering, output structuring, guardrails, evals, tracing/observability, and more.
• Stakeholder and Product Collaboration: Engage confidently with technical and non-technical stakeholders alike, translating fuzzy asks into actionable development items, and navigating requirements changes with a proactive approach.
• AI Adoption Support: Deliver targeted enablement sessions to help teams organization-wide maximize the value of AI tools, advise leadership on adoption strategies, flag risk factors, and help create policy around best practices and responsible AI use.
• Documentation & Enablement: Develop runbooks, internal docs, and practical user guides for internal tools and processes. Bridge the technical-non-technical gap by articulating what systems can and can''t do, especially in failure scenarios.

• End-to-End Product Engineering: Experience designing, building, and shipping complex products with minimal guidance; autonomy in translating ambiguous asks into delivered features.

• Agentic LLM Systems: Proficiency with LangGraph or LangChain, prompt engineering, tool/function calling, system guardrails, evaluation frameworks, observability, and related concepts for LLM-driven workflows.

• RAG System Expertise: Practical hands-on experience with RAG pipelines (chunking, embedding model selection, hybrid search, reranking, retrieval evaluation) and relevant vector DBs (ChromaDB, Pinecone, Typesense, pgvector, Weaviate, FAISS).

• Full-Stack Development:

• Back End: Advanced Python (FastAPI, Flask, Django, Pydantic), solid grounding in OOP, functional patterns, concurrency (asyncio, threading), virtual environments, testing frameworks (pytest, unittest).

• Front End: React, TypeScript, SCSS, modern build tools (Vite, Webpack), in-depth with custom hooks, context/state management, routing, component lifecycle, and performance optimization.

• Architectural Fluency: Deep familiarity with common architectural patterns (MVC, layered/clean architecture, event-driven design), and the ability to justify application in different scenarios.

• Real-Time System Design: Experience with SSE, WebSockets, streaming responses, job queues, and scalable background worker design.

• API Mastery: REST and GraphQL design, evolution-resilient internal/external API contracts.

• Database Proficiency: PostgreSQL, MySQL/MariaDB, NoSQL, vector datastores-versed in strengths, trade-offs, and appropriate use cases.

• Caching & Performance: Redis, edge and query-layer caching strategies, and cache invalidation patterns.

• Scaling and Optimization: Load & scale testing, data modeling for performance, traffic analysis.

• Security and Auth: JWT/OAuth flows, auth middleware, and experience with tools like Auth.js, Firebase Auth, Supabase Auth, and SSO providers.

• DevOps: GitHub Actions, CI/CD pipelines, YAML, infrastructure as code/config.

• Server/Infrastructure: AWS, Nginx/Apache, Gunicorn, Ubuntu, Bash; bonus for Kubernetes (deployments, services, basic Helm) and debugging containerized environments.

• Modern AI Coding Workflows: Enthusiastic adopter of AI coding assistants and aware of trade-offs, extensive usage, and effective orchestration of these tools as part of the engineering process.

• Stakeholder Engagement: Skilled in discovery conversations with non-technical users, able to navigate ambiguous asks and surface root needs. Decisive when architectural choices aren''t fully prescripted, comfortable pushing back or clarifying direction as needed.

• Communication: Clarity in communicating trade-offs, risks, and realistic timelines to both technical and non-technical audiences.

• Secondary and Nice to Haves: PHP (server-side APIs, OOP, namespacing, Composer, modern PHP syntax)

• NodeJS (AdonisJS or comparable frameworks)

• Kubernetes and container orchestration (Helm, configmaps/secrets, debugging)

• ML/algorithmic skills (classical ML concepts, Python ML stack, model evaluation)-though primary ML expertise is covered by an SME

• React Native (for mobile platforms)

• Personal Attributes: Relishes autonomy-takes initiative to own problems end-to-end and deliver without regular hand-holding

• Optimizes for shipping the right solution quickly rather than striving for an elusive perfect architecture

• Pushes back with reasoned alternatives when technical asks are misaligned with broader goals

• Comfortable in healthy, supportive, low-burnout work cultures; does not seek or reward burnout-level commitment, * ~5+ years experience as a product engineer or full-stack engineer, ideally with direct exposure to agentic LLM systems in production.

• Demonstrable experience delivering full product cycles-requirements through production - in environments with minimal supervision.

• Expertise in Python and React/TypeScript stack with associated tooling and frameworks.

• History of adopting and championing AI-powered development workflows.

• Education- Technical Bachelors or higher