Sr.Data Scientist

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.

1. 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.
2. 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).
3. 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.
4. 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).
5. Governance & Ethics :
   • Enforce transparency, fairness, and compliance (GDPR, SOC2) in AI-driven workflows.
   • Monitor model drift and performance in production systems.