Full Stack ML Engineer

• End-to-End Model Development: Lead the complete lifecycle of deep learning models, from initial data exploration and model design to training, rigorous evaluation, optimization, and deployment into production environments.
• Advanced ML/DL Application: Design, develop, and implement state-of-the-art machine learning and deep learning algorithms for diverse applications, with a strong focus on Computer Vision and Natural Language Processing.
• Generative AI Expertise: Drive the integration and development of Generative AI solutions, leveraging frameworks and techniques such as LangChain, LangGraph, Retrieval Augmented Generation (RAG), and advanced embedding strategies to create innovative capabilities.
• Multi-Modal Solutions: Develop and deploy multi-modal AI solutions, including expertise in image embedding and fusing information from various data types.
• Data Pipeline Collaboration: Collaborate closely with data engineering teams to ensure efficient data access, robust data pipelines, and scalable infrastructure for model training and inference.
• MLOps & Reproducibility: Apply best practices in MLOps, including extensive use of Docker for containerization, and implement robust package management and versioning strategies to ensure reproducibility and maintainability of models.
• Prompt Engineering: Utilize advanced prompt engineering techniques to fine-tune and optimize the performance, behavior, and output quality of large language models and other generative AI systems.
• Independent & Accountable Work: Demonstrate a high level of maturity and autonomy, capable of independently driving projects from conception through to successful completion.
• Solution Defense & Reasoning: Possess strong analytical and reasoning capabilities to articulate, present, and defend proposed machine learning solutions and technical decisions to both technical and non-technical stakeholders.
• Agile Collaboration: Work effectively within an Agile development methodology, adapting to evolving requirements, participating actively in sprint cycles, and collaborating seamlessly with cross-functional teams.
• Validation & Testing Excellence: Implement rigorous model validation, testing, and quality assurance processes to ensure the accuracy, reliability, and performance of all developed AI solutions.

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Electrical Engineering, or a related quantitative field.
• Proven professional experience as a Data Scientist or Machine Learning Engineer, with a significant focus on deep learning.
• Demonstrated experience in the end-to-end training, evaluation, and deployment of deep learning models in a production setting.
• Expertise in classical Machine Learning, Deep Learning, Computer Vision, and Natural Language Processing.
• Hands-on experience with Generative AI concepts and tools, including LangChain, LangGraph, RAG, and various embedding techniques.
• Knowledge on multi-modal solutions, specifically involving image embedding.
• Basic understanding of data pipeline architectures and data engineering principles.
• Proficiency with MLOps tools such as Docker for containerization and best practices in package management and versioning.
• Experience with prompt engineering for optimizing AI model performance.
• Strong Python programming skills are essential, with extensive experience in:

• PyTorch (mandatory)
• OpenCV
• Pandas
• Scikit-learn
• Scikit-image
• Hugging Face Transformers
• NLTK
• Sentence-Transformers
• Other related data science and machine learning packages.

Ford