Senior Software Engineer - AI App Enablement & Observability

AI in this role

Build and scale core AI platforms, observability pipelines, and automated feedback loops for AI applications.

opentelemetryllm-gatewaysapissdks

observabilitytelemetryai-platformsdistributed-tracingmlops

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. Discover what makes Bloomberg unique - watch our podcast series for an inside look at our culture, values, and the people behind our success.

How we rate this

Senior Software Engineer - AI App Enablement & Observability at Bloomberg rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

ObservabilityTelemetryAI PlatformsDistributed TracingMlopsOpentelemetryLLM GatewaysApis

Questions you could be asked

Tell me about a project where observability was part of your work. What did you do?
Tell me about a project where telemetry was part of your work. What did you do?
Tell me about a project where ai platforms was part of your work. What did you do?
Tell me about a project where distributed tracing was part of your work. What did you do?
Tell me about a project where mlops was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: Observability, Telemetry, AI Platforms, Distributed Tracing, and Mlops. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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