Principal AI/ML Architect

Egen is a rapidly growing technology company driven by a data-first approach. We combine top-tier engineering talent with advanced platforms like Google Cloud and Salesforce to help clients transform data and insights into impactful actions. Our culture fosters innovation, continuous learning, and solving complex problems with speed and precision. We seek individuals who are passionate about leveraging data, AI, and technology to create meaningful change.

As a Principal AI/ML Architect at Egen, you will lead the technical vision for our most challenging generative AI, agentic AI, and applied machine learning projects. You will remain hands-on, treating AI as a disciplined software engineering practice. Your role involves architecting scalable, reliable systems that succeed in production and mentoring engineers to achieve the same standards. You will tackle complex problems such as agentic platforms at scale, advanced retrieval systems, and AI applications in regulated or high-stakes environments. From ambiguous business needs to robust, secure services, you will ensure solutions meet stringent requirements around latency, cost, governance, and security.

You will also act as a senior technical advisor, collaborating closely with client leadership to align AI architecture with business strategy. Your influence will extend from pre-sales through delivery, guiding engineering teams and fostering long-term client partnerships.

Key Responsibilities

- Set enterprise AI architecture and technical direction: Own end-to-end architecture for complex generative AI and ML platforms. Define reusable patterns, engineering guardrails, and technology standards across projects.

- Build at the frontier – hands-on: Engage directly with high-impact technical work including foundation model fine-tuning, advanced retrieval-augmented generation (RAG), semantic search, and agentic workflows using Python and Google Cloud Vertex AI.

- Engineer AI for production: Design systems optimized for latency, reliability, security, cost, scalability, and maintainability. Implement disciplined MLOps and LLMOps practices for versioning, monitoring, deployment, and continuous improvement.

- Architect agentic systems at scale: Develop multi-step reasoning and multi-agent workflows with tools like Google ADK, LangChain, and custom frameworks, ensuring robust orchestration, state management, human oversight, and failure isolation.

- Establish evaluation, observability, and responsible AI standards: Define measurable criteria and telemetry for accuracy, 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 covering data ingestion, embeddings, vector search, re-ranking, provenance, permissions, and content refresh.

- Advise clients and shape opportunities: Collaborate with client executives and engineering leaders to translate strategy into AI roadmaps and architectures. Lead discovery workshops, technical estimates, trade-off discussions, and pre-sales solutioning.

- Multiply engineering capability: Elevate team performance through architecture reviews, mentoring, reusable accelerators, and clear standards that empower engineers to deliver confidently.

- Own outcomes across the full lifecycle: Guide projects from problem framing through production rollout, operational readiness, adoption, and measurable business impact. Proactively identify risks and drive resolution.

Required Qualifications

- Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field, or equivalent practical experience.

- 15+ years of progressive experience in software engineering, machine learning, data, or AI, with significant ownership of production systems at scale.

- Proven principal-level technical leadership including architecture ownership and cross-team decision-making on complex engagements.

- Demonstrated success deploying generative AI, agentic AI, or applied ML products into production beyond proofs of concept.

- Deep hands-on expertise with Python, modern LLM and ML architectures, agent frameworks, Google Cloud/Vertex AI, production APIs, and scalable software systems.

- Experience establishing evaluation, observability, security, privacy, governance, and responsible AI controls for enterprise AI solutions.

- Strong data engineering and SQL skills, with the ability to design reliable data and retrieval foundations for AI products.

- Senior client-facing experience with excellent communication skills to explain technical decisions, risks, and business value to executives and engineering teams.

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 public sector.

- Relevant certifications such as Google Cloud Professional Machine Learning Engineer or Professional Cloud Architect.

- Experience building reusable AI accelerators, reference implementations, or platform capabilities across multiple clients or products.

- Pre-sales experience including discovery workshops, solution shaping, estimation, proposal development, and technical presentations.

- Familiarity with Kubernetes, infrastructure as code, event-driven architectures, and modern data platforms.

Egen may use AI tools to assist in parts of the hiring process, such as reviewing applications and assessing responses. These tools support but do not replace human judgment. Final hiring decisions are made by our recruitment team.