[Hiring] Chief Python AI Solution Engineer @EPAM

Role Description

We are seeking a Chief Python AI Solution Engineer to build an enterprise AI agent platform and need you to define the multi-agent architecture, governance, and security controls from day one. You will lead solution design and delivery across Python, FastAPI, Google Agent Development Kit, and Google Cloud Platform while guiding engineers toward scalable, compliant services.

• Define multi-agent system architecture and orchestration patterns for specialized AI agents
• Design agent-to-service communication, tool contracts, and structured output interfaces
• Establish governance for LLM routing, safety controls, and cost/token usage limits
• Architect financial and compliance guardrails, including segregation-of-duties workflows
• Lead platform integration patterns for action routing, API invocation, and event-driven flows
• Set engineering standards for coding, testing, API contracts, and schema compliance
• Mentor engineers through design reviews, code reviews, and delivery planning
• Coordinate with architects and product stakeholders to align scope, risks, and timelines

Qualifications

• 7+ years of Python software engineering experience on production systems
• 2+ years of technical leadership experience guiding engineers and owning architecture decisions
• Strong solution architecture skills for distributed services and AI platforms
• Advanced FastAPI expertise for designing and operating reliable APIs
• Advanced Google Agent Development Kit experience for agent design, tools, and orchestration
• Strong Google Cloud Platform experience deploying and operating cloud-native workloads
• Strong communication skills to align stakeholders and translate requirements into designs
• Upper-Intermediate English proficiency (B2) for collaboration and documentation

Requirements

• Nice to have: Agentic Automation Engineering experience in enterprise environments
• Technical Leadership experience leading multi-team delivery
• Docker experience for containerized builds and deployments
• Google Kubernetes Engine experience operating production workloads
• Google Cloud BigQuery experience designing analytics-friendly data models