DE&A - AIML - Deep Learning - Generative AI

• Design & Development:

• Lead the design, development, and implementation of end-to-end AI/ML systems and pipelines, from data ingestion to model deployment and monitoring.
• Develop and optimize algorithms and models, with a strong emphasis on Generative AI models (LLMs, Diffusion Models, etc.), fine-tuning, and prompt engineering.
• Write clean, maintainable, and efficient code in Python and other relevant languages.
• MLOps & Deployment:

• Implement robust MLOps practices for model versioning, continuous integration/continuous deployment (CI/CD) of ML models, monitoring, and retraining.
• Deploy AI/ML models into production environments, ensuring scalability, reliability, and performance.
• Manage and optimize cloud infrastructure (e.g., AWS, Azure, GCP) for AI/ML workloads.
• Research & Innovation:

• Stay abreast of the latest advancements in AI/ML, particularly in Generative AI, large language models (LLMs), and relevant frameworks/libraries.
• Propose and evaluate new AI technologies and methodologies to enhance existing products and explore new opportunities.
• Contribute to the strategic direction of our AI roadmap.
• Data & Feature Engineering:

• Collaborate with data scientists and data engineers to collect, clean, preprocess, and transform large datasets for model training.
• Develop effective feature engineering strategies to improve model performance.
• Collaboration & Communication:

• Work cross-functionally with product management to understand business needs and translate them into technical specifications.
• Partner with software engineers to integrate AI solutions into existing products and platforms.
• Mentor junior AI/ML engineers and share knowledge within the team.
• Performance & Optimization:

• Monitor and troubleshoot deployed AI models for performance, accuracy, and bias.
• Optimize model inference for speed and cost efficiency.

• Education: Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Electrical Engineering, or a related quantitative field.
• Experience: 5-7 years of professional experience in an AI Engineer, Machine Learning Engineer, or similar role, with a proven track record of deploying AI/ML solutions to production.
• Programming: Strong proficiency in Python is essential. Experience with other languages like Java, C++, or Go is a plus.
• ML/DL Frameworks: Hands-on experience with major machine learning and deep learning frameworks (e.g., PyTorch, TensorFlow, Keras, Hugging Face Transformers).
• Generative AI Expertise:

• Demonstrable experience with Large Language Models (LLMs), including fine-tuning, prompt engineering, RAG (Retrieval Augmented Generation), and understanding of various model architectures (e.g., GPT, BERT, Llama).
• Experience with other generative models (e.g., Stable Diffusion, GANs) is a significant advantage.
• MLOps & Cloud:

• Solid understanding and practical experience with MLOps tools and practices (e.g., MLflow, Kubeflow, DVC, Airflow).
• Proficiency with at least one major cloud platform (AWS, Azure, GCP) for deploying and managing ML workloads (e.g., SageMaker, Azure ML, Google AI Platform).
• Experience with containerization (Docker) and orchestration (Kubernetes).
• Data Handling: Experience with data querying languages (SQL) and working with large datasets.

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