Full-time
Your Role
- Support Enablement & Readiness:
- Analyze service onboarding processes to uncover bottlenecks, knowledge gaps, and manual steps that slow time-to-support.
- Deploy agentic AI capabilities to assist with documentation discovery, runbook navigation, and contextual knowledge synthesis.
- Standardize onboarding assets and readiness criteria, reducing reliance on tribal knowledge and SME dependency.
- Operational Toil Reduction:
- Identify recurring operational pain points and evaluate them as candidates for automation.
- Assess the feasibility and impact of proposed automation ideas and maintain a prioritized backlog of improvement initiatives.
- Drive execution and track outcomes against measurable targets.
- Agentic Operations:
- Validate agentic use cases across detection, triage, investigation, remediation, and documentation workflows.
- Map current operational workflows to surface automation gaps and build a roadmap for a scalable agentic operations ecosystem.
- Proactively generate new use cases that complement existing initiatives and maximize operational impact.
- Visibility, Insights & Dashboards:
- Design and maintain operational dashboards that surface real-time and trend-based insights across workflows and service health.
- Translate raw operational data into clear, decision-ready visualizations for engineers and leadership.
- Identify blind spots in current observability and collaborate with stakeholders to continuously improve what gets measured.
- AI Onboarding & Future Initiatives:
- Drive AI tool adoption across the team through enablement, documentation, and hands-on guidance.
- Explore emerging AI capabilities and assess their fit for unmet operational needs.
Your Qualifications
- Bachelor's degree in Computer Science, Mathematics, Applied Physics, Data Science, or a related field
- 2 to 4 years in data engineering, platform engineering, MLOps, or operations-focused technical role.
- Hands-on experience building automation solutions or data pipelines in a production environment.
- Exposure to AI/ML tooling, agentic frameworks, or LLM-based applications is a strong plus.
- Comfortable working in ambiguous environments, able to identify problems and propose solutions independently.
Operational Outcomes
- Systems thinking: can zoom out to see the operational picture, then zoom in to implement a targeted solution.
- Automation mindset: instinctively asks can this be automated before accepting manual work as the norm.
- Data fluency: comfortable with SQL, querying operational data, and turning metrics into insights.
- Clear communication: able to explain technical solutions to non-technical stakeholders and document work for future maintainability.
- Collaborative: works well across engineering, operations, and leadership without needing constant direction.
- Understands agentic AI concepts and design patterns including tool use, memory, multi-agent orchestration, and human-in-the-loop workflows and can translate these into practical operational use cases.
Plus Points
- Proficient in Python, Pandas, Polars, and Apache Spark.
- Hands-on experience with LangChain, LangGraph, RAG pipelines, and prompt engineering.
- Familiar with PostgreSQL, Apache Iceberg
- Understand end-to-end AI architecture - RAG, agentic systems, multi-model workflows, APIs, and production deployment.
- Knowledgeable in ethical AI, bias mitigation, transparency, AI governance, and enterprise regulatory considerations.
- Strong grasp of data modeling, feature stores, dimensional modeling, and dataset structure for ML pipelines.
Ready to start your awesome journey and be part of OpsWerks
