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.
Responsibilities
Key duties and responsibilities
Implement and operate integration of AI capabilities into enterprise products following standard patterns
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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
Qualifications
Required experience & competencies
5–8 years in software/ML engineering
Cloud (Azure), Kubernetes, Python
Experience with GenAI and ML systems
Technical Skills
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Strong hands-on experience in:
Python, APIs, microservices architecture
Cloud environments (Azure preferred, AWS/GCP acceptable)
Kubernetes and containerized deployments
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Experience with:
MLOps / LLMOps tooling
Monitoring/observability tools (e.g., logs, metrics, tracing)
Data pipelines and distributed systems
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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
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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.