AI Operations Lead

Job Summary

The AI Operations Lead contributes hands-on to integration, deployment and monitoring of AI systems such as ML, GenAI, RAG and agentic workflows. The role focuses on a subset of products/services and ensures operational excellence, observability, and performance.

Key duties and responsibilities

-

Implement and operate integration of AI capabilities into enterprise products following standard patterns

-

Contribute to deployment of

• -
• RAG pipelines
• -
• Copilots and AI assistants
• -
• Agentic workflows
• -
• Predictive ML services
• -
• Support delivery squads in integrating AI services into business applications
• -
• roubleshoot and resolve integration or runtime issues in production
• AI Observability & Monitoring (Core focus)

• Design and implement AI observability frameworks, including:
• Model performance monitoring (drift, quality, hallucination signals)
• Usage and adoption of metrics
• Latency, reliability, and health
• Ensure proper logging, tracing, and monitoring of AI pipelines
• Contribute to definition of AI SLAs/SLOs aligned with business expectations

• -
• Support incident management and post-mortem analysis for AI systems

Cost & Performance Optimization

• Monitor AI-related cloud consumption and inference costs
• Optimize pipelines for efficiency (model selection, caching, orchestration)
• Contribute to FinOps practices specific to AI workloads

Business Acumen

• -
• Understand operational impact of AI systems on business processes
• -
• Able to balance performance, cost, and quality trade-offs
• -
• Communicates effectively with technical and business stakeholders
• Required experience & competencies
• -
• 5–8 years in software/ML engineering
• -

Cloud (Azure), Kubernetes, Python

• -
• Experience with GenAI and ML systems

Technical Skills

• Strong hands-on experience in:
• Python, APIs, microservices architecture
• Cloud environments (Azure preferred, AWS/GCP acceptable)
• Kubernetes and containerized deployments
• Experience with:
• MLOps / LLMOps tooling
• Monitoring/observability tools (e.g., logs, metrics, tracing)
• Data pipelines and distributed systems
• Understanding of:
• GenAI / LLM systems (RAG, embeddings, prompting)
• ML lifecycle and deployment patterns

Soft skills

• Hands-on and problem-solving mindset
• Ability to debug complex AI systems in production
• Strong collaboration with engineering and product teams
• Ability to explain technical issues clearly to non-experts
• Proactive and continuous improvement mindset

Business acumen

• Can adapt his/her speech to make relevant for business users
• Can interact effectively with top management
• Can support in produce presentations or architecture material