The posting
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:
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-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.
- 15+ years of progressive experience across software engineering, machine learning, data, or AI, including substantial ownership of production systems at scale.
- Demonstrated principal-level technical leadership, including architecture ownership, cross-team technical direction, and decision-making across complex engagements or product portfolios.
- A proven record of deploying generative AI, agentic AI, and/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 knowledge, with the ability to design reliable data and retrieval foundations for AI products.
- Senior client-facing experience, with the communication skills to explain technical choices, risks, and business value to executive stakeholders 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 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.
