Senior Software Engineer - AI App Enablement & Observability

Platform Engineering builds the core platforms, tooling, and paved roads that Bloomberg engineers rely on to ship reliable, secure, and high-performing systems at scale. The AI App Enablement & Observability team accelerates how AI products are built across Bloomberg Industry Group. Our mission is to make AI systems reliable, performant, cost-efficient, and continuously improving through platform tooling, deep observability, and automated feedback loops. We build developer-facing platforms and workflows that enable teams to experiment, deploy, and operate AI and agent-based systems with confidence. This includes LLM gateways, agent platforms, benchmarking systems, telemetry pipelines, and self-improving infrastructure that closes the loop between observability and action. We emphasise strong developer experience, intuitive APIs/SDKs, and end-to-end ownership. What’s in it for you? You will help define how Bloomberg Industry Group builds and operates AI systems at scale by working on platforms that: Accelerate AI product development through reusable tooling and paved roads Provide end-to-end observability across AI systems (models, agents, pipelines, applications) Enable self-improving systems through telemetry-driven feedback loops Optimise cost, performance, and reliability of AI workloads Support both production AI systems and internal engineering agents You’ll collaborate across AI product, infrastructure, and platform teams to deliver foundational systems. We’ll trust you to: Platform & Enablement Build and evolve AI platform tooling (e.g., developer workflows, benchmarking systems) Design developer-friendly APIs, SDKs, and interfaces Contribute to systems across the Model Development Lifecycle (experimentation, deployment, evaluation) Observability & Telemetry Build and operate observability platforms and telemetry pipelines (logs, metrics, traces, events) Provide visibility into latency, token usage, cost, quality, drift, and reliability Define instrumentation standards, schemas, and conventions Implement distributed tracing using modern approaches (e.g., OpenTelemetry) AI System Insights & Debugging Enable end-to-end debugging of AI and agent workflows (model calls, tool usage, retrieval, orchestration) Build benchmarking, regression detection, and performance analysis capabilities Support observability for both production systems and internal engineering agents Closed-loop Optimization & Automation Develop systems that turn telemetry into action (automated experimentation, regression detection, alerting) Build feedback loops that continuously improve model quality and system behavior Enable self-healing and self-optimising workflows Cost, Performance & Reliability Build tooling for cost visibility, forecasting, and optimization Define SLOs, alerting, and performance tuning practices Improve reliability and scalability of AI infrastructure Ownership & Collaboration Own projects end-to-end (RFCs, architecture, implementation, rollout, production support) Partner with AI teams to drive adoption of platform tooling and standards Produce high-quality documentation and improve developer experience

Demonstrated experience building production software or platform systems Strong engineering fundamentals with distributed systems or backend platforms Experience or strong interest in observability and debugging complex systems Experience or strong interest in AI/ML systems, LLMs, or agent-based architectures Strong ownership mindset and ability to drive ambiguous problems to production Hands-on experience with modern agentic coding tools and multi-model workflows Working knowledge of agent architecture internals (context engineering, tool loops, sub-agent orchestration) We’d love to see: Experience with OpenTelemetry and modern observability ecosystems, including instrumentation, collectors, exporters, and tools like Prometheus, Grafana, and tracing/log systems Experience designing and operating telemetry pipelines, including sampling, retention, cardinality, and cost tradeoffs, as well as integrating observability into CI/CD and developer workflows Familiarity with AI/agent frameworks, including instrumentation of LLM calls, tool usage, workflows, and evaluation signals (quality metrics, benchmarking, regression detection) Experience building cost monitoring, forecasting, and optimization systems for AI workloads Familiarity with cloud and infrastructure tooling (e.g., AWS, Azure, Kubernetes, Terraform) Experience with agentic infrastructure concepts such as MCP servers, hooks, skills, subagents, sandboxing, and persistent memory patterns Active engagement with the agentic engineering frontier, including emerging patterns (e.g., harness vs. model, review debt, feedback loops) Demonstrated agent-native development practices (iterating with agents using testing, verification, and feedback loops) Strong security awareness for autonomous systems, including sandboxing, prompt injection risks, credential exposure, and guardrails If indicated, please note that years of experience are a guide; we will consider applications from all candidates who can demonstrate the skills necessary for the role. Discover what makes Bloomberg unique - watch our podcast series for an inside look at our culture, values, and the people behind our success.