Full Stack ML Engineer

In this Role, you will be at the forefront of developing and deploying advanced machine learning and deep learning solutions. Closely work with Data Engineers and other ML engineers in the team. This pivotal role involves leading the entire lifecycle of AI models, from research and experimentation to robust production deployment. You will tackle complex challenges across various domains, including Computer Vision, Natural Language Processing, and cutting-edge Generative AI applications, contributing directly to our next generation of intelligent products and services. We are seeking a highly motivated individual who can work independently, champion their solutions, and thrive in an agile, results-oriented environment.

Responsibilities:

• 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.

Required Qualifications:

• 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.