Software Engineer - AI Platform Services

Key Responsibilities

• Design, build, and operate core platform services (Python; REST + async; streaming where appropriate) powering firm-wide internal AI/agentic capabilities.
• Own gateway/platform concerns end-to-end: routing, timeouts/retries, streaming, request shaping, rate limits/quotas, multi-tenancy, policy enforcement, provider abstraction, safe degradation, and robust client experience.
• Build and operate MCP capabilities as part of the platform.
• Build for scale and availability on Kubernetes: autoscaling, rollout strategies, capacity planning, performance tuning, and production debugging.
• Raise reliability practices: define and manage SLOs/SLIs, instrumentation standards, incident response/runbooks, post-incident follow-ups, load/resilience testing, and operational excellence.
• Improve delivery safety: CI/CD, environment promotion, IaC-driven repeatability, and secure SDLC practices.
• Influence roadmap and technical strategy: prioritize foundational investments and reduce platform risk for a business-critical internal platform.

Required Qualifications

• 7+ years of professional software engineering experience (or equivalent practical experience)
• Strong expertise in Python, Java, or Go, including async patterns, concurrency, and building high-throughput services (FastAPI or similar).
• Solid distributed systems fundamentals: idempotency, backpressure, failure isolation, consistency tradeoffs, rate limiting, retries/timeouts.
• Production experience operating services on Kubernetes (deployments, autoscaling, debugging, observability, performance).
• Basic familiarity with LLM integration patterns (streaming responses, tool/function calling)
• Demonstrated design leadership (RFCs, architecture reviews, leading cross-team initiatives).
• Excellent communication skillsable to translate technical tradeoffs to stakeholders and partner teams.

Preferred Qualifications

• Experience with service-to-service authentication patterns (API keys, OAuth/JWT, mTLS concepts).
• Familiarity with observability tooling (structured logs, metrics, tracing; Datadog or OpenTelemetry a plus).
• Strong fundamentals in AWS (or GCP/Azure) relevant to secure platforms (IAM, networking basics, compute, logging/monitoring patterns).
• Working proficiency with Terraform and automation-first operations (repeatable environments, policy checks, safe rollouts).
• Comfort using AI dev tools (Claude Code, Cursor, Gemini CLI) responsibly (tests, validation, secure coding).