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. 

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

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