Lead Platform Engineering

We are looking for a Lead Platform Engineer to build and operate shared AI platform services that power agentic workflows, including LLM gateways, MCP tooling, and observability by default. You will enable teams with secure, scalable Kubernetes-based delivery, deep telemetry, and cost visibility.
Responsibilities
Build shared AI platform services such as LLM gateways, proxy layers, routing, fallback, and supporting APIs with telemetry and cost attribution
Operate and scale platform services on Kubernetes using GitOps workflows with Helm and Kustomize, including progressive delivery and autoscaling
Onboard model providers and model versions across environments with routing rules, quotas, tiering, fallback behavior, and deprecation paths
Design and run the MCP layer, including server deployment, tool registration and discovery, session handling, and safe permission boundaries
Implement authentication, authorization, and tenancy using OAuth2/OIDC, SSO, JWT, API keys, and managed secrets
Instrument AI platform behavior end to end using OpenTelemetry, metrics, and structured logs alongside application-level AI traces
Implement Langfuse telemetry patterns for traces, spans, prompts, completions, feedback, evaluations, latency, errors, and token usage
Build dashboards, alerts, and reports for AI reliability, performance, quality, and evaluation outcomes
Design cost and usage observability across LLM vendors with attribution by app, team, user, model, workflow, and environment
Create showback or chargeback-ready metrics and feed insights into routing, tiering, and capacity decisions
Apply policy guardrails including PII detection, filtering, audit logging, and retention controls for prompts, completions, and traces
Enable the agent lifecycle with publishing, versioning, registry discovery, memory management, resilience, and explainability signals
Build self-service onboarding and provisioning workflows, templates, and portals or CLIs for teams
Partner with engineering teams to define and enforce observability standards as the default delivery path
Requirements
5+ years platform engineering experience
Strong leadership skills to set standards and mentor engineers
Proven project ownership for designing and operating shared internal platforms
Advanced Kubernetes skills for running production services
Strong AI platforms experience across model gateways, routing, and agent runtimes
Hands-on Langfuse experience for AI tracing and evaluation metadata
Practical Model Context Protocol (MCP) experience with servers, registries, and tool boundaries
Strong OpenTelemetry skills for traces, metrics, and structured logs
Solid security knowledge in OAuth2/OIDC, SSO, JWT, and secrets management
Excellent stakeholder communication skills with application and developer teams
Upper-Intermediate English proficiency (B2)
Nice to have
LangChain experience for agent workflow integration and analysis
LangGraph experience for graph-based agent orchestration
Retrieval-Augmented Generation (RAG) experience including evaluation and tuning
We offer
International projects with top brands
Work with global teams of highly skilled, diverse peers
Healthcare benefits
Employee financial programs
Paid time off and sick leave
Upskilling, reskilling and certification courses
Unlimited access to the LinkedIn Learning library and 22,000+ courses
Global career opportunities
Volunteer and community involvement opportunities
EPAM Employee Groups
Award-winning culture recognized by Glassdoor, Newsweek and LinkedIn
EPAM is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, age, sexual orientation, gender identity or expression, disability, protected veteran status, or any other characteristic protected by applicable law.

Work arrangement
Yes