Senior AI Architect

About Dotwork:

Dotwork connects strategy, goals, capital, work, and outcomes in a live enterprise operating model — giving leaders and AI the context to make better decisions, adapt faster, and continuously improve performance. As a Senior AI Architect, you'll design the agent and skill infrastructure that lets AI reason over that model at enterprise scale, and act on it only where people have approved.

Role Overview:

You'll own the architecture for how AI agents and skills work inside Dotwork: how they get context from a large, governed enterprise graph, how they call tools, how they're evaluated, and how they stay inside the decisions people have delegated to them. Our customers bring large volumes of finance, cloud billing, HR, portfolio and architecture data. Your job is to make agents fast, accurate and trustworthy on top of it.

You will work directly on customer problems, turning repeatable operating work — planning, cost allocation, portfolio reviews, architecture decisions — into reusable skills and agents built on the Dotwork platform, for large, innovative organizations. Many of which are household names.

Technologies:

• AI & Agents: LLM APIs (Anthropic, OpenAI and others), agent frameworks, MCP, tool and function calling, retrieval and embeddings, evaluation tooling
• Data: Dgraph, Postgres, large-scale ingestion and transformation pipelines, event streams
• Backend: GoLang, gRPC, NATS
• DevOps/Cloud: AWS, Docker, Kubernetes, Infrastructure-as-Code

Key Responsibilities:

• Architect the runtime for Dotwork AI agents and skills: context assembly, tool access, memory, orchestration, and human approval points.
• Design how agents retrieve and reason over a large enterprise graph and high-volume operational data, balancing accuracy, latency and cost.
• Build and extend Dotwork's MCP server and skill interfaces so agents, ours and our customers', can work with the model safely.
• Define evaluation, observability and guardrails for agent behavior: offline evals, production tracing, regression testing, and cost per task.
• Set the patterns for packaging repeatable work into reusable skills that customers and our field team can configure and extend.
• Partner with data and platform engineering on the pipelines that bring billions of rows of finance, cloud, HR and work data into the model.
• Track AI token spend and model selection so every agent's cost sits beside the value it delivers.
• Provide hands-on technical leadership and mentorship; raise the bar for how the team builds with AI.
• Prototype and iterate quickly with customers, then harden what works for production.

Ideal Candidate:

• Agent Architecture Expertise – Proven experience designing and shipping LLM agents, tool-calling systems or skill frameworks in production, not just demos.
• Big Data Fluency – You've built systems over very large, messy enterprise datasets and know how to get the right context to a model without drowning it.
• Strong Systems Design Skills – You architect robust, maintainable services and optimize for reliability, latency and cost.
• Evaluation Discipline – You measure agent quality rigorously, and you know the difference between "it worked once" and "it works."
• Governance Mindset – You design for permissions, auditability and human judgment from the start; agents prepare and propose, and people decide.
• Hands-On Builder – You still write code, review it closely, and use AI tools daily in your own workflow.
• Mentorship & Communication – You lead through influence and explain complex trade-offs clearly to engineers, customers and executives.
• Curiosity & Purpose – Motivated by the opportunity to improve how organizations execute strategy and make decisions.

Preferred Qualifications:

• 10+ years of professional software engineering experience, including 3+ years building with LLMs or applied ML.
• Experience with MCP, agent frameworks, or building your own orchestration layer.
• Background in data platforms, data engineering or distributed systems at enterprise scale.
• Experience with graph databases (Dgraph or similar) and relational databases (Postgres).
• Experience in GoLang or another backend language (Java, .NET, Python).
• Familiarity with AWS services (e.g., ECS, RDS, S3, Bedrock) or GCP equivalents.
• Exposure to enterprise finance, FinOps, technology business management or portfolio management domains.
• Experience with infrastructure-as-code, CI/CD and automated testing practices.

What We Offer:

• A unique opportunity to define how AI agents work inside the enterprise, on a live model of how the business actually runs.
• The chance to use cutting-edge AI tools daily, both in your personal workflow and integrated customer solutions.
• A collaborative and visionary culture where we want everyone to feel they're doing the most challenging, but best work of their careers.
• Competitive compensation, benefits, and a flexible work environment.