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.