Senior ML Engineer

Essential functions

• AI Agent Infrastructure: Design, implement, and maintain secure, scalable AI agent infrastructure on Google Cloud using Vertex AI and modern agent orchestration frameworks.

• Operationalizing AI (AIOps): Build and enforce automated model deployment, continuous monitoring workflows, and model lifecycle management across the platform.

• Identity & Access Governance: Develop and enforce secure IAM frameworks, including role-based access control (RBAC), least-privilege configurations, and cross-cloud alignment (Azure AD and Google Workspace).

• Model Execution Boundaries: Review, stage, and secure custom MCP (Model Context Protocol) actions, ensuring safe, compliant, and well-bounded model execution.

• CI/CD & Infrastructure Automation: Architect and manage automated compliance gates, CI/CD pipelines, and infrastructure deployment utilizing Terraform and GitHub Actions.

• Observability & Monitoring: Implement robust real-time observability, policy enforcement, and alerting systems for containerized AI workflows to ensure maximum system reliability.

Qualifications

• Cloud AI Mastery: Deep hands-on experience with cloud-based ML/AI platforms, specifically Google Cloud, Vertex AI, and AI Agent engines.

• Agentic Frameworks: Proven track record of building and orchestrating intelligent agents using tools like the Google Cloud Agent Development Kit (ADK) and Model Context Protocol (MCP).

• Enterprise Security & Architecture: Strong familiarity with enterprise-grade security concepts, including workload identity federation, short-lived tokens, VPC, and private endpoints.

• Infrastructure as Code (IaC): Advanced skills in Terraform and CI/CD workflows (GitHub Actions) to ensure infrastructure is tightly controlled during agent deployment.

• Coding & Scripting: Strong proficiency in Python for backend development, AI automation, and handling distributed message workflows via Google Pub/Sub.

• Observability Tools: Practical experience setting up enterprise observability and monitoring stacks (e.g., OpenTelemetry, Grafana, LangSmith, PagerDuty).

Would be a plus

• Containerization & Scale: Hands-on experience with serverless architectures and containerized environments, specifically Google Kubernetes Engine (GKE) and Docker.

• Big Data Ecosystem: Familiarity with high-throughput data storage and search engines like Elasticsearch and BigTable.

• Policy-as-Code: Understanding of real-time policy enforcement and frameworks like OPA (Open Policy Agent).

• Retail or Supply Chain Domain: Experience engineering high-load, distributed systems within large-scale retail, supply chain logistics, or e-commerce platforms.

We offer

• Opportunity to work on bleeding-edge projects
• Work with a highly motivated and dedicated team
• Competitive salary
• Flexible schedule
• Benefits package - medical insurance, sports
• Corporate social events
• Professional development opportunities
• Well-equipped office

About us

Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.