About The Role

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 metrics
Latency, reliability, and system 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

Understands 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

Required Education

Master’s degree (Ph. D. is a plus) in Science, Technology, Engineering, Computer Science,
Bachelor’s degree plus ASA or similar work experience is accepted in place of a relevant Master’s degree
Certifications on Cloud or Microservices or Kubernetes (CKAD) are plus.