Summary:

As an ML Engineer II on the AI, Automation & Data team, you will independently design, deploy, operate, and continuously improve production machine learning, generative AI, and agent-enabled solutions that deliver measurable business value. This hands-on role combines end-to-end ML product ownership with ownership of defined components within shared MLOps, Feature Store, model-observability, AgentOps, and GenAIOps capabilities.

You will lead moderately complex implementations, mentor junior team members and interns, and work with ML Engineers, Data Engineers, Data Scientists, Architects, Product and Domain Owners, and business partners. Enterprise architecture, organization-wide technical strategy, platform roadmaps, and final governance authority remain with Senior, Lead, and Architecture roles.

Core Responsibilities:

• Own small-to-medium ML, generative AI, and agent-enabled solutions through design, implementation, deployment, monitoring, support, performance optimization, lifecycle management, and continuous improvement.

• Design and lead moderately complex production ML implementations using established architecture patterns and approved Google Cloud services, including Vertex AI and BigQuery where appropriate.

• Own and enhance defined components of shared MLOps, Feature Store, model-observability, AgentOps, GenAIOps, deployment, or reusable ML-service capabilities that support multiple products and teams.

• Develop modular, reusable, secure, and testable Python solutions for data preparation, feature engineering, model development, packaging, deployment, validation, monitoring, and automation.

• Improve MLOps pipelines, CI/CD patterns, templates, controls, and engineering accelerators using Git, peer review, automated testing, documentation, versioning, and operational standards.

• Lead monitoring and troubleshooting for model performance, drift, reliability, data quality, and operational health; coordinate remediation, incident response, and appropriate escalation.

• Lead model documentation, validation, and production-readiness activities while ensuring adherence to established responsible AI, legal, compliance, security, architecture, and engineering requirements.

• Partner with Data Engineers to design, validate, and improve trusted, reusable features and datasets, and use SQL and BigQuery for exploration, reconciliation, feature preparation, and performance analysis.

• Build and evaluate models using appropriate techniques, metrics, validation, explainability, and reproducibility practices; guide less-experienced engineers in applying these practices.

• Design and operationalize GenAI, retrieval-augmented generation, and agent-enabled solutions using approved enterprise models, data, tools, APIs, and guardrails.

• Review code, technical designs, deployment approaches, and AI-assisted outputs; provide actionable feedback and ensure alignment with team standards and maintainability expectations.

• Mentor junior team members and interns through onboarding, technical coaching, pair development, code reviews, design reviews, and knowledge sharing.

• Communicate design decisions, delivery status, production health, risks, and recommendations to technical and non-technical stakeholders; participate in agile planning and cross-functional delivery.

Required Qualifications:

• Bachelor's or master’s degree in computer science, data science, engineering, information systems, or a related field, or equivalent practical experience.

• Five or more years of relevant experience in ML engineering, MLOps, data science, software engineering, or a closely related discipline.

• Advanced Python proficiency and strong SQL proficiency for model development, production engineering, automation, validation, troubleshooting, and performance optimization.

• Demonstrated experience independently designing, deploying, monitoring, supporting, and improving production ML solutions.

• Demonstrated experience with production MLOps concepts and practices, including CI/CD, packaging, deployment, versioning, reproducibility, observability, and lifecycle management.

• Experience owning or materially enhancing reusable ML platform components, frameworks, services, templates, or engineering accelerators.

• Experience with Git-based source control, automated testing, peer code reviews, technical documentation, APIs, and modular software-engineering practices.

• Working knowledge of cloud architecture and hands-on experience with cloud-based ML and data services; familiarity with BigQuery or a comparable cloud data warehouse.

• Experience leading technical reviews and mentoring less-experienced engineers, interns, or project contributors.

• Strong analytical, troubleshooting, incident-management, communication, and cross-functional collaboration skills.

Preferred Qualifications:

• Hands-on experience with Vertex AI or a comparable managed ML platform, including pipelines, registries, endpoints, or monitoring.

• Experience with reusable Feature Store capabilities, shared model-observability services, or multi-product MLOps platform components.

• Experience with scikit-learn, TensorFlow, PyTorch, XGBoost, or comparable ML frameworks.

• Experience designing or operationalizing generative AI, retrieval-augmented generation, agent frameworks, AgentOps, or GenAIOps capabilities.

• Familiarity with containers and additional Google Cloud services used for serverless processing, integration, orchestration, APIs, or monitoring, such as Cloud Run.

• Experience supporting responsible AI, model risk, privacy, security, legal, compliance, or architecture reviews for production ML or AI solutions.

Team & Culture:

• Collaborates effectively with ML Engineers, Data Engineers, Data Scientists, Architects, Product and Domain Owners, and business partners across onshore and offshore teams.

• Demonstrates ownership, adaptability, accountability, sound technical judgment, and commitment to reliable production outcomes.

• Provides constructive technical guidance, welcomes feedback, and helps develop Engineer I resources and interns without formal people-management accountability.

• Supports knowledge sharing, responsible AI, reusable engineering, disciplined operations, and continuous improvement aligned with business objectives and technical standards.

• Influences technical execution within defined areas of ownership while partnering with Senior, Lead, and Architecture roles on enterprise direction and roadmap decisions.