Jobs at Egen

Lead enterprise AI architecture for generative AI, agentic AI, and machine learning engagements. Design scalable production systems, advanced RAG and retrieval platforms, multi-agent workflows, evaluation and observability standards, and responsible AI controls. Remain hands-on with Python, Vertex AI, model tuning, and agent frameworks. Advise executive clients, lead discovery and pre-sales, guide engineering teams, and drive initiatives from experimentation through production and measurable business outcomes.

Leads and mentors application engineers while designing Java and Spring Boot microservices. Oversees application modernization, VM-to-container migration, GCP and GKE deployments, Kubernetes troubleshooting, CI/CD, testing, database integration, observability, and distributed systems. Coordinates technical dependencies across a large application portfolio and collaborates with platform, DevOps, security, and architecture teams. Uses AI-assisted engineering tools to accelerate development, modernization, testing, documentation, and automation.

Designs and maintains Azure enterprise data infrastructure using Terraform and CI/CD pipelines. Implements multi-plane architectures, hybrid Azure-Google Cloud networking, secure platform services, Kubernetes workloads, identity and access controls, and centralized monitoring. The role supports Airflow, Milvus, Neo4j, GPU workloads, and healthcare-compliant environments with HIPAA, PHI, encryption, data residency, and secure SDLC requirements.

Build and operate distributed GPU training platforms for foundation models. Run and debug multi-node PyTorch and Ray Train workloads, tune GPU networking and scheduling, profile performance, and develop benchmarks and evaluation harnesses. Partner with infrastructure teams and client scientists to improve throughput, reliability, cost efficiency, observability, and end-to-end training pipelines on GKE.

Defines enterprise data architecture, modernization, governance, and migration strategies across GCP, Azure, hybrid, and on-premises environments. Designs lakehouse platforms, agent-driven migration pipelines, streaming and batch ingestion, governance, security, and data quality solutions. Advises customers, manages technical backlogs and roadmaps, leads architecture reviews, mentors engineering teams, and performs hands-on prototyping and performance tuning.

Designs and builds data ingestion, feature engineering, analytics, and machine learning pipelines using healthcare and other structured or unstructured data. Develops, tunes, deploys, monitors, and documents predictive models and enterprise data products. Collaborates with data scientists, engineers, product managers, and stakeholders while ensuring data quality, reliability, and production engineering standards.

Build and maintain full-stack applications that deliver AI/ML insights through backend services, REST APIs, containerized microservices, and high-performance front-end interfaces. Responsibilities include cloud-native development on Google Cloud or Azure, database management, testing, CI/CD automation, security, troubleshooting, and Agile collaboration.

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.

Owns public-sector sales and revenue growth across State, Local, and Higher Education accounts. Responsibilities include managing the full sales lifecycle, developing account strategies, owning P&L and bookings targets, building executive relationships, partnering with Google Cloud, scoping modernization projects, drafting Statements of Work, and supporting regional marketing. The role focuses on cloud, data, AI, and analytics modernization opportunities, particularly across California agencies and institutions.

Leads a span of approximately 20 engineers while shaping hiring, onboarding, career development, staffing, and engineering enablement across the AI practice. The role is also approximately 50% billable client delivery, providing executive advisory on AI software development lifecycles, governance, agentic deployments, MLOps and LLMOps pipelines, cloud platforms, observability, and production resilience. Requires extensive engineering leadership, organizational scaling, consulting, GCP, and production AI/ML deployment experience.

Lead organizational change and adoption for enterprise AI and cloud transformations. Develop change strategies, assess impacts, coach executives, manage resistance, activate change champions, design training and communications, facilitate workshops, measure adoption, and sustain new behaviors. Translate technical AI concepts into business value and guide Gemini Enterprise pilots using Google Workspace and related tools.

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.

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.

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.