De&a - Aiml - Deep Learning - Generative Ai

**Key Responsibilities**:

- **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.

**Required Qualifications**:

- **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.

**Problem-Solving**:Excellent analytical and problem-solving skills, with the ability to translate complex business problems into technical AI solutions.