Machine Learning Engineer

Job Description: ML Engineer / Gen AI Engineer

Client

Location

Position Overview

ML Engineer / Gen AI Engineer

Key Responsibilities

• Develop and Optimize ML Models

  : Design, implement, and fine-tune machine learning models using Python libraries such as TensorFlow, PyTorch, or scikit-learn to solve real-world financial problems.

• Graph-Based Solutions

  : Build and optimize graph-based algorithms and data structures using libraries like NetworkX or PyG (PyTorch Geometric) for applications such as network analysis, fraud detection, or knowledge graphs.

• SQL and Data Management

  : Write complex SQL queries to manage, transform, and analyze large datasets, ensuring efficient data pipelines and integration with databases like PostgreSQL, MySQL, or SQLite.

• Generative AI Development

  : Design and implement generative AI models (e.g., LLMs, GANs, or VAEs) using frameworks like Hugging Face, LangChain, or custom solutions for tasks such as text generation, data augmentation, or synthetic data creation.

• Code Quality and Scalability

  : Write clean, modular, and maintainable Python code, adhering to best practices, and optimize for performance and scalability in production environments.

• Collaboration and Innovation

  : Work closely with data scientists, engineers, and business teams at UBS to translate requirements into technical solutions, contributing to architectural decisions and innovative AI strategies.

• Data Pipeline Development

  : Build and maintain ETL pipelines to preprocess and integrate data for machine learning and graph-based applications, using tools like Apache Airflow or Pandas.

• Model Deployment

  : Deploy machine learning and generative AI models to production environments using tools like Docker, Kubernetes, or cloud platforms (AWS, GCP, Azure).

• Research and Stay Current

  : Stay updated on advancements in machine learning, graph theory, and generative AI, applying cutting-edge techniques to enhance project outcomes.

Required Qualifications

• Education

  : Bachelor's or Master's degree in Computer Science, Data Science, Mathematics, or a related field. PhD is a plus.

• Experience

  :
• 5+ years of professional Python programming experience.
• 3+ years of hands-on experience in machine learning model development and deployment.
• Proven expertise in graph algorithms, graph databases (e.g., Neo4j), or graph-based machine learning.
• Strong proficiency in SQL and relational database management.
• 2+ years working with generative AI models (e.g., LLMs, GANs, or diffusion models).

Technical Skills

• Expert-level Python programming (e.g., Pandas, NumPy, scikit-learn, TensorFlow, PyTorch).
• Experience with graph libraries (e.g., NetworkX, PyG, or DGL) and graph databases.
• Advanced SQL skills for querying and optimizing large datasets.
• Familiarity with generative AI frameworks (e.g., Hugging Face, LangChain, or OpenAI APIs).
• Proficiency in version control (Git), CI/CD pipelines, and containerization (Docker).
• Experience with cloud platforms (AWS, GCP, or Azure) for model deployment.

Soft Skills

• Strong problem-solving and analytical skills.
• Excellent communication and collaboration abilities.
• Ability to work in a fast-paced, innovative environment.

Preferred Qualifications

• Experience with large-scale distributed systems and big data frameworks (e.g., Spark, Hadoop).
• Familiarity with MLOps tools (e.g., MLflow, Kubeflow) for model lifecycle management.
• Knowledge of advanced graph algorithms (e.g., community detection, shortest path, centrality measures).
• Contributions to open-source AI or graph-related projects.
• Experience with real-time or streaming data processing.
• Familiarity with financial services or banking domain challenges.

Why Join Us

• Work on cutting-edge AI and graph-based projects with real-world impact in the financial sector.
• Collaborate with a talented, global team in a supportive and innovative environment at UBS.
• Competitive salary, comprehensive benefits, and opportunities for professional growth.
• Access to state-of-the-art tools and technologies to fuel your expertise.