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.