Senior AI Ops Engineer - Application
$114,080 - $218,030
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Posted yesterday
SENIORITY
Lead
SALARY
$114,080 - $218,030
About the role
- Build and operate high-throughput ingestion and feature pipelines that consume application logs, traces, APM data, and business-transaction events - collected by the Observability & Telemetry team - into the AIOps feature store.
- Train, validate, and tune anomaly-detection and forecasting models against application and transaction-level signals, and build LLM-based log parsing, summarization, and root-cause-hypothesis components with explicit handling for hallucination risk.
- Design and build event correlation and probable-cause ranking logic that reduces raw application alert volume reaching a human.
- Write, test, and version remediation automation for application-layer failure modes, with tested rollback for every action, operating within the approval-gate, blast-radius, and kill-switch framework.
- Instrument and monitor your own models and automations in production, retraining on a defined cadence, and participate in the on-call rotation for any live action at autonomy level 3 or above.undefined
- What you have:
- Bachelor's degree; OR 4 years of relevant education and/or experience.5+ years of hands-on software or data engineering experience, with strong programming ability in Python plus a second production language - code that ships, is tested, and is code-reviewed, not just notebooks.
- Practical experience building and productionizing ML models - anomaly detection, classification, or forecasting - using frameworks such as TensorFlow, PyTorch, or scikit-learn, including the full lifecycle from training through retraining.
- Working fluency with LLM-based techniques - prompt design, retrieval-augmented generation, structured outputs - and a clear-eyed understanding of their failure modes.
- Experience writing and testing operational automation - workflow orchestration, runbook-as-code - including rollback design and approval-gated or policy-controlled execution.
- Comfort operating in a regulated environment, with demonstrated ability to debug and reason about a live production incident under time pressure.
- What sets you apart:
- Proven experience deploying model-driven root-cause analysis or incident triage solutions used in production application operations.
- Experience building production automation with tested guardrails, safety controls, and kill-switch capabilities.
- Experience developing AI evaluation and monitoring frameworks, including regression testing, model and prompt evaluation, and drift detection.
- Experience with event correlation, alert grouping, and high-volume application telemetry, including exposure to enterprise AIOps platforms such as Moogsoft, BigPanda, or Dynatrace.
- Experience working with model governance or model risk teams and supporting model validation through appropriate documentation, including model cards.
- US military experience gained through military service or gained as a military spouse / domestic partner
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