Technical Specialist - Machine Learning/Deep Learning

In your role as a Technical Specialist in the IT Products and DevOps team, your key responsibilities will include: - Developing AI & ML Models: You will design and implement machine learning and deep learning models to address complex business challenges, such as predictive analytics, natural language processing (NLP), and computer vision. - Designing ML Models: You will create ML models for tasks like classification, regression, recommendation, and forecasting. - Fine-Tuning LLMs: You will optimize transformer-based large language models (LLMs) like GPT, LLaMA, and Mistral using techniques such as LoRA, PEFT, and RLHF. - Building ML Pipelines: Your role involves constructing end-to-end ML pipelines for data preprocessing, training, evaluation, and deployment. - Data Processing & Analysis: Collaborating with data engineers, you will collect, clean, and preprocess extensive datasets through automated data engineering pipelines (DataOps) for model training and evaluation. - Monitoring Models: You will oversee deployed models to detect drift, accuracy, and latency issues, implementing feedback loops and performance benchmarks. - Model Optimization & Tuning: Continuously enhancing model performance by adjusting hyperparameters, feature engineering, and applying advanced techniques like transfer learning. - Deployment: Integrating and deploying ML & AI models into production environments (Azure, AWS, GCP) to ensure scalability, reliability, and security. - Collaboration: Engaging closely with cross-functional teams, including product managers, software engineers, data scientists, and other stakeholders, to grasp business needs and convert them into ML & AI solutions. - Research & Innovation: Staying updated on ML algorithm advancements, recent AI research, and DevOps practices like MLOps, AIOps, LLMOps. Contributing to the evolution of new algorithms, tools, and techniques for enhancing the efficiency and effectiveness of AI models. - Documentation & Reporting: Documenting model designs, workflows, and methodologies for internal knowledge sharing and regulatory compliance. - Setting Standards: Working with leadership to establish data, ML, and LLM engineering practices and ensure adherence across teams. - Release Processes: Ensuring effective communication and documentation of all release processes, policies, and procedures. - Time Management: Capable of meeting tight deadlines, prioritizing multiple requests simultaneously. Qualifications Required: - 9 to 12 years of experience with a minimum of 7 years dedicated to Azure Cloud Deployment, managing ML, DL & AI applications. - Proficiency in Cloud platforms like Azure, AWS, and GCP. - Expertise in deployment tools such as FastAPI, Docker, Kubernetes, MLOps, MLflow, LLMOps, Azure DevOps CI/CD pipelines, and automation. - Strong programming skills in Python and SQL. - Familiarity with ML libraries like scikit-learn, PyTorch, XGBoost, LightGBM, TensorFlow. - Experience with deep learning tools like Keras API and GenAI tools like Hugging Face Transformers, LangChain, OpenAI API. - Knowledge of LLM models such as GPT, LLaMA, Gemini, BERT, Mistral, Falcon, and fine-tuning techniques like LoRA, PEFT, RLHF, prompt tuning. - Proficiency in data engineering tools like Pandas, NumPy, Spark. - Understanding of databases (SQL/NoSQL) and big data technologies such as Hadoop and Spark. - Familiarity with NLP tasks, evaluation metrics, supervised, unsupervised, reinforcement learning, and computer vision. - Knowledge of Descriptive, Diagnostic, Predictive, and Prescriptive Analytics. - Azure ML & Data Engineer certification would be an added advantage. - Effective communication skills and analytical thinking with problem-solving abilities. In your role as a Technical Specialist in the IT Products and DevOps team, your key responsibilities will include: - Developing AI & ML Models: You will design and implement machine learning and deep learning models to address complex business challenges, such as predictive analytics, natural language processing (NLP), and computer vision. - Designing ML Models: You will create ML models for tasks like classification, regression, recommendation, and forecasting. - Fine-Tuning LLMs: You will optimize transformer-based large language models (LLMs) like GPT, LLaMA, and Mistral using techniques such as LoRA, PEFT, and RLHF. - Building ML Pipelines: Your role involves constructing end-to-end ML pipelines for data preprocessing, training, evaluation, and deployment. - Data Processing & Analysis: Collaborating with data engineers, you will collect, clean, and preprocess extensive datasets through automated data engineering pipelines (DataOps) for model training and evaluation. - Monitoring Models: You will oversee deployed models to detect drift, accuracy, and latency issues, implementing feedback loops and performance benchmarks. - Model Optimization &