AI Engineer

We are looking for an AI Engineer who thrives at the intersection of technology and operations. This is not a role in building AI for its own sake, it is about applying AI, automation, analytics, and systems thinking to drive real, measurable operational outcomes.

You will work across service onboarding, toil reduction, agentic workflows, and operational visibility, turning pain points into scalable solutions that make the entire team faster and more resilient.

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 if you have:

• 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.