This role combines advanced AI/ML expertise with IT operations (Infrastructure, Networks, Applications, Digital integrated experience) to build scalable solutions for automating processes like incident management, resource allocation, and infrastructure optimization. Key objectives include:

Reducing mean time to resolution (MTTR) through predictive maintenance and AI-driven anomaly detection. Deploying Gen AI for log analysis, code generation, and automated documentation using frameworks like GPT-4, LangChain, or Vertex AI. Designing Agentic AI systems where autonomous agents collaborate to execute tasks (e.g., auto-scaling, security threat mitigation). Integrating AI with DevOps pipelines (CI/CD, Kubernetes) and IT tools (ServiceNow, Ansible, Terraform). 5+ years in data science, with 3+ years focused on AI-driven IT automation. Proficiency in Python, PyTorch/TensorFlow, LLM frameworks, and cloud platforms (AWS/GCP/Azure). Advanced degree in Computer Science, Data Science, or related fields. Certifications in AI/ML or MLOps (e.g., AWS ML Specialty, Google Cloud ML Engineer) preferred. AI/ML Model Development: Build predictive models for IT automation (e.g., incident prediction, resource optimization). Implement reinforcement learning for self-healing infrastructure and auto-scaling. Gen AI Integration: Develop LLM/RAG-based conversational AI for automated code reviews, log parsing, and knowledge-base curation. Fine-tune models for domain-specific tasks (e.g., IT ticket classification, root-cause analysis). Agentic AI Design: Architect multi-agent systems to automate workflows like ticket routing, patch management, and compliance checks. Enable real-time collaboration between AI agents and human operators. Automation Pipeline Engineering: Deploy AI models into production using MLOps tools (MLflow, Kubeflow) and orchestration platforms (Airflow, Prefect). Ensure seamless integration with DevOps pipelines and observability tools (Datadog, Splunk). Governance & Ethics: Enforce transparency, fairness, and compliance (GDPR, SOC2) in AI-driven workflows. Monitor model drift and performance in production systems.