Jobs at Egen

Leads the design, architecture, development, testing, and support of scalable ETL and ELT data pipelines using Python, PySpark, Databricks, GCP, and Azure. Responsibilities include data ingestion, transformation, quality validation, Medallion and Delta Lake architecture, Spark optimization, cloud migrations, workflow orchestration, CI/CD, documentation, and direct customer requirements gathering. The role also collaborates with data scientists, analysts, and engineering teams to deliver reliable enterprise data solutions.

Owns strategic enterprise accounts across the West region, advising executive stakeholders and driving multi-million-dollar growth. Leads account strategy, P&L, solution scoping, commercial negotiations, cloud/data/AI transformation opportunities, cross-functional delivery, and alliance co-selling with Google Cloud. Develops client success stories and account-based marketing initiatives while ensuring measurable business outcomes and successful engagement delivery.

Own strategic enterprise accounts and drive multi-million-dollar growth by advising executives on data, AI/ML, and cloud transformation. Lead account strategy, solution design, scoping, pricing, negotiations, delivery orchestration, and alliance co-selling with Google Cloud and other partners. Build relationships with Fortune 500 and Fortune 1000 clients, exceed sales quotas, close new professional services business, and connect technical solutions to measurable business outcomes.

Leads the architecture, design, and implementation of scalable GCP and Databricks data lakehouse platforms. Designs real-time and batch pipelines, data models, governance, cataloging, security, and performance optimization. Advises clients, translates business requirements into technical solutions, leads project teams, mentors engineers, reviews architectures, and balances strategic planning with hands-on proof-of-concept and troubleshooting work.

Lead data engineering and machine learning initiatives by designing, architecting, developing, and supporting GCP data pipelines. Create information architecture documentation, estimate and plan technical work, collaborate with project teams and business analysts, and translate requirements into technical designs. Develop Python-based ELT pipelines, optimize BigQuery SQL, support CI/CD, and provide technical leadership to projects and teams.

Lead design and build of production Generative AI solutions (document intelligence, RAG, semantic search). Implement and optimize LLMs, embeddings, and agentic workflows, deploy on Google Cloud/Vertex AI, apply MLOps, monitor performance, and collaborate with clients to translate requirements into scalable, cost‑efficient AI systems.

Create high-fidelity motion graphics, static visuals, animated product demos, case studies, sales collateral, social content, and campaign assets. Translate complex technology concepts into clear, engaging visual narratives while maintaining brand consistency. Own projects from storyboarding and concept development through final export and asset management, coordinating multiple priorities with strong attention to detail and technical quality.

Lead hands-on cloud implementation and technical ownership for Google Cloud environments: design, deploy, and operate secure, scalable infrastructure; implement hybrid connectivity, security guardrails, observability, and automation; lead delivery and mentor engineering teams.

Drive revenue by designing tailored technical solutions, delivering product demos, writing proposals and RFP responses, validating integrations, managing stakeholder expectations, and supporting transition and delivery as a hands-on Forward Deployed Engineer (50% strategic delivery time). Serve as subject-matter expert in industry positioning and thought leadership.

As a Senior AI Engineer, you will develop and optimize Generative AI applications, manage AI pipelines, collaborate with clients, and ensure production delivery with MLOps best practices.

Lead enterprise cloud strategy and architecture on Google Cloud, drive multi-cloud interoperability, enforce governance and security, optimize cost, deliver large-scale implementations (GKE, BigQuery, Vertex AI), mentor senior engineers, and promote GitOps and automation across the delivery lifecycle.

Lead architecture and hands-on implementation of large-scale Generative AI and agentic systems. Build production-ready RAG, embedding fine-tuning, vector search, and MLOps pipelines on Google Cloud. Advise clients in pre-sales, mentor engineers, and ensure reliability, latency, cost, and responsible-AI controls for high-stakes deployments.