AI Engineer

• 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.

• 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.