Technical Specialist - Machine Learning/Deep Learning

You are required to join the IT Products and DevOps team as a Technical Specialist focusing on building scalable Machine Learning (ML) systems and customizing Deep Learning (DL) / large language models (LLMs) using automation through MLOps and LLMOps. **Responsibilities:** - Design and implement machine learning and deep learning models for predictive analytics, NLP, and computer vision. - Develop ML models for classification, regression, recommendation, and forecasting. - Fine-tune transformer-based LLMs using techniques like LoRA, PEFT, and RLHF. - Optimize prompts for LLMs to enhance task-specific performance. - Create end-to-end ML pipelines for data preprocessing, training, evaluation, and deployment. - Collaborate with data engineers for data gathering, cleaning, and preprocessing via automated data engineering pipelines (DataOps). - Monitor deployed models for drift, accuracy, and latency, implementing feedback loops and performance benchmarking. - Continuously improve model performance through hyperparameter tuning, feature engineering, and advanced techniques like transfer learning. - Integrate and deploy ML & AI models into production environments, ensuring scalability, reliability, and security. - Work closely with cross-functional teams to understand business requirements and translate them into ML & AI solutions. - Stay updated with the latest ML algorithms, AI research, DevOps practices, and trends. Contribute to the development of new algorithms and tools. - Document model designs, workflows, and methodologies for internal knowledge sharing and regulatory compliance. - Set standards for Data, ML, LLM engineering practices within the team and support across other disciplines. - Communicate and document all release processes, policies, and procedures effectively. - Demonstrate the ability to handle and prioritize simultaneous requests and meet tight deadlines. **Qualifications:** - 9 to 12 years of experience with a minimum of 7 years in Azure Cloud Deployment, maintaining, and managing ML, DL & AI applications. - Proficiency in Azure Cloud with familiarity in AWS & GCP. - Strong programming skills in Python and SQL. - Experience with deployment tools like FastAPI, Docker, Kubernetes, MLOps, MLflow, LLMOps, Azure DevOps CI/CD pipelines, and automation. - Familiarity with ML libraries such as scikit-learn, PyTorch, XGBoost, LightGBM, TensorFlow, and Deep Learning using Keras API. - Knowledge of GenAI Tools like Hugging Face Transformers, LangChain, OpenAI API, and LLM Models such as GPT, LLaMA, Gemini, BERT, Mistral, Falcon. - Experience in data engineering tools like Pandas, NumPy, Spark, and knowledge of databases (SQL/NoSQL) and big data technologies. - Understanding of NLP tasks, evaluation metrics, Supervised, Unsupervised, Reinforcement learning, computer vision, and Descriptive, Diagnostic, Predictive, and Prescriptive Analytics. - Azure ML & Data Engineer certification would be an added advantage. - Excellent communication skills and analytical thinking with problem-solving abilities. You are required to join the IT Products and DevOps team as a Technical Specialist focusing on building scalable Machine Learning (ML) systems and customizing Deep Learning (DL) / large language models (LLMs) using automation through MLOps and LLMOps. **Responsibilities:** - Design and implement machine learning and deep learning models for predictive analytics, NLP, and computer vision. - Develop ML models for classification, regression, recommendation, and forecasting. - Fine-tune transformer-based LLMs using techniques like LoRA, PEFT, and RLHF. - Optimize prompts for LLMs to enhance task-specific performance. - Create end-to-end ML pipelines for data preprocessing, training, evaluation, and deployment. - Collaborate with data engineers for data gathering, cleaning, and preprocessing via automated data engineering pipelines (DataOps). - Monitor deployed models for drift, accuracy, and latency, implementing feedback loops and performance benchmarking. - Continuously improve model performance through hyperparameter tuning, feature engineering, and advanced techniques like transfer learning. - Integrate and deploy ML & AI models into production environments, ensuring scalability, reliability, and security. - Work closely with cross-functional teams to understand business requirements and translate them into ML & AI solutions. - Stay updated with the latest ML algorithms, AI research, DevOps practices, and trends. Contribute to the development of new algorithms and tools. - Document model designs, workflows, and methodologies for internal knowledge sharing and regulatory compliance. - Set standards for Data, ML, LLM engineering practices within the team and support across other disciplines. - Communicate and document all release processes, policies, and procedures effectively. - Demonstrate the ability to handle and prioritize simultaneous requests and meet tight deadlines. **Qualifications:** - 9 to