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 Git Ops 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 Open Telemetry, 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

Requirements5+ 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 Open Telemetry 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 Linked In Learning library and 22,000+ courses

Global career opportunities

Volunteer and community involvement opportunitiesEPAM Employee Groups Award-winning culture recognized by Glassdoor, Newsweek and Linked In