Key Responsibilities:

• AI-Driven IT Infrastructure Optimization: Develop predictive analytics models to monitor and enhance IT system performance.
  Implement anomaly detection algorithms for security, fault detection, and intrusion prevention. Use AI to automate IT operations, including incident response, log analysis, and system diagnostics.
• Machine Learning Model Development & Deployment: Design and train AI/ML models for IT infrastructure automation, cloud resource optimization, and workload balancing.
  Implement LLMs and NLP to enhance IT ticketing systems and self-healing .Deploy AI models into cloud-based and on-prem IT environments using MLOps best practices.
• Data Science & Engineering: Collect, pre-process, and analyze large-scale IT infrastructure logs, telemetry, and system health data.
  Build and maintain real-time data pipelines for AI-driven IT operations (AIOps). Utilize big data tools (Hadoop, Spark, Kafka) for large-scale infrastructure analytics.
• Collaboration & Innovation: Work with IT teams, DevOps, and security engineers to integrate AI solutions into existing infrastructure.
  Research and experiment with Generative AI models (e.g., GPT, BERT, LLaMA) to improve IT workflows and automation.
  Stay updated on emerging AI trends in IT operations, cybersecurity, and cloud management.

Requirements

Qualifications – External

To be successful in this role you should meet the following requirements: 

• Bachelor’s degree in engineering, Information Technology, or a related field (or equivalent experience).
• 10+ year Hands-on experience with machine learning frameworks (e.g., TensorFlow, PyTorch) and generative AI tools (e.g., Hugging Face, OpenAI, LangChain).
• Design, develop, and deploy machine learning models to solve business challenges, including predictive analytics, recommendation systems, and optimization problems.
• Analyse large, complex datasets to uncover trends, patterns, and insights that inform business strategies and product development.
• Clean, pre-process, and transform raw data into formats suitable for analysis and modelling.
• Deep understanding of machine learning algorithms (e.g., regression, classification, clustering, deep learning) and generative AI techniques (e.g., GANs, VAEs, transformers).
• Strong programming skills in Python, R, or similar languages.
• Experience with data manipulation and analysis using SQL, Pandas, NumPy, etc.
• Familiarity with big data technologies (e.g., Hadoop, Spark).