Principal AI/ML Architect

About Egen:

Egen is a fast-growing, entrepreneurial technology company with a data-first mindset. We bring together exceptional engineering talent and advanced technology platforms, including Google Cloud and Salesforce, to help clients drive action and impact through data and insights. 

We are committed to being a place where talented people choose to do their best work - applying deep engineering and technology expertise to envision how data, AI, and platforms can change the world for the better. We are dedicated to learning, thrive on solving difficult problems, and continually innovate to achieve fast, effective results. If that describes you, we want you on our team.

About the opportunity:

As a Principal AI/ML Architect, you will set the technical direction for Egen’s most complex generative AI, agentic AI, and applied machine learning engagements while remaining personally hands-on in the work that matters most. We treat AI as a software engineering discipline. You will raise that bar by architecting systems that succeed in production and helping the engineers around you do the same.

You will own the hardest architectural problems in our portfolio: agentic platforms operating at scale, applied-ML solutions, advanced retrieval systems, and AI in regulated or high-stakes environments where being wrong is expensive. Document intelligence and RAG pipelines are part of your toolkit, not the ceiling. You will take systems from an ambiguous business problem to a robust, scalable service, solving for latency, reliability, cost, security, and governance.

You will also be the senior technical face of our engagements. You will partner directly with client leadership to translate business strategy into AI architecture, shape solutions during pre-sales, guide engineering teams, and earn the trust that turns a project into a long-term program.

What you will do:

• Set enterprise AI architecture and technical direction: Own the end-to-end architecture for complex GenAI, agentic AI, and ML platforms. Define reference architectures, reusable patterns, engineering guardrails, and technology choices across engagements.
• Build at the frontier - hands-on: Stay close to the code on the highest-leverage technical work, including foundation-model and embedding fine-tuning, advanced RAG, semantic search, evaluation harnesses, and novel agentic workflows using Python and Google Cloud Vertex AI.
• Engineer AI for production: Design for latency, reliability, security, cost, scalability, and maintainability from day one. Apply disciplined MLOps and LLMOps practices so systems are versioned, evaluated, monitored, deployed, and continuously improved.

• Architect agentic systems at scale: Design and operate multi-step reasoning and multi-agent workflows using ADK, LangChain, MCP, and custom agent frameworks, with robust orchestration, state management, tool use, memory, human oversight, failure isolation, and verification controls.

• Establish evaluation, observability, and responsible AI standards: Define measurable quality criteria and production telemetry for accuracy, groundedness, safety, drift, latency, and cost. Embed privacy, security, governance, and responsible AI throughout the lifecycle.
• Shape data and retrieval architectures: Design production-scale retrieval and knowledge systems spanning data ingestion, chunking, metadata, embeddings, vector and hybrid search, re-ranking, provenance, permissions, and continuous content refresh.
• Advise clients and shape opportunities: Partner with client executives and engineering leaders to translate business strategy into AI roadmaps and solution architectures. Lead discovery, technical workshops, estimates, trade-off discussions, and pre-sales solutioning.
• Multiply engineering capability: Raise the technical bar through architecture reviews, design documents, code reviews, mentoring, reusable accelerators, and clear standards that help senior and mid-level engineers deliver with greater confidence.
• Own outcomes across the full lifecycle: Guide initiatives from problem framing and experimentation through production rollout, operational readiness, adoption, and measurable business value. Surface risks early and drive decisions to resolution.

Your technical toolkit:

Languages and software engineering 

Expert-level Python; strong shell scripting; API and service design; clean, tested, maintainable code; distributed-sys…