Job Description

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.

Your technical toolkit

Languages and software engineering

Expert-level Python; strong shell scripting; API and service design; clean, tested, maintainable code; distributed-system fundamentals; and full SDLC ownership.

Models and AI ecosystem

Deep, current expertise with Google Gemini, GPT-class models, LLaMA and other open models; advanced prompt engineering; structured outputs; model selection; fine-tuning; and rigorous evaluation.

Agentic AI engineering

Hands-on production experience with Google Agent Development Kit (ADK), LangChain, custom agent frameworks, tool use, Model Context Protocol (MCP), workflow orchestration, planning, memory, state, guardrails, human-in-the-loop controls, and multi-agent design.

Retrieval and search

Advanced RAG, hybrid and semantic search, embeddings, re-ranking, and vector platforms such as Vertex AI Vector Search, pgvector, Pinecone, or equivalent technologies.

Cloud infrastructure and AI systems

End-to-end experience architecting highly available, scalable AI and software environments across Google Cloud / Vertex AI, AWS, and Azure, including model serving, data integration, IAM, networking, security, observability, and cost management.

Frameworks

Strong command of LangChain, LlamaIndex, or equivalent orchestration layers - together with the judgment to use simpler, more maintainable patterns when appropriate.

MLOps and LLMOps

CI/CD, model and prompt versioning, experiment tracking, automated evaluation, observability, model monitoring, drift detection, rollback, and production incident response.

Data and ML foundations

Strong SQL and data engineering; classical machine learning; neural networks; training, tuning, and inference; and the ability to connect data quality to model and product outcomes.

Basic qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related technical field; equivalent practical experience will also be considered.

Preferred qualifications

• Experience training, tuning, or adapting neural networks, embedding models, or foundation models.
• Architecture experience in regulated or high-stakes domains such as healthcare, financial services, or the public sector.
• Google Cloud Professional Machine Learning Engineer, Professional Cloud Architect, or comparable certification.
• Experience building reusable AI accelerators, reference implementations, or platform capabilities across multiple clients or product teams.
• Pre-sales experience including discovery workshops, solution shaping, estimation, proposal development, and technical presentations.
• Familiarity or good to have understanding with Kubernetes, infrastructure as code, event-driven architectures, and modern data platforms.