Data & AI Operations Engineer.

Key Responsibilites

• Apply AI/ML/LLM tools to solve operational and reliability challenges
• Prototype and validate AI?driven improvements with measurable outcomes
• Operationalise successful AI use cases into scalable production systems
• Enhance observability, incident detection, and automation in operations
• Support end-to-end lifecycle of AIOps, MLOps, and LLMOps solutions
• Collaborate with engineers and ops teams to align solutions with business needs
• Stay ahead of emerging AI technologies and embed responsible AI practices
• Build prototypes and measure impact with clear metrics, analyze logs, metrics, and events to detect anomalies and automation opportunities. Deploy, monitor, and maintain ML/LLM solutions
• Automate repetitive tasks with scripts/workflows. Document solutions and lessons learned for reuse

Key Requirements

• Bachelor's/Master's in Computer Science or related field
• 1–2 years' experience with Python, SQL, Spark, Docker/Kubernetes, Git, pytest
• Exposure to MLOps/GenAI Ops tools, ML/GenAI projects, or open-source contributions
• Familiarity with monitoring stacks (Prometheus, Grafana, OpenTelemetry)
• Hands-on with cloud ML services (Databricks, Azure ML, etc.)
• Curious, proactive, collaborative, and impact-driven