Senior Software Engineer - AI App Enablement & Observability

Location Dublin Business Area Engineering and CTO Ref # 10050677 Description & Requirements 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 You’ll Need To Have - 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. 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