Lead Machine Learning Engineer, GFT

Come and be part of our cutting-edge and high performing GenAI and Mobile Team if you are a talented, tenacious, meticulous and results-focused individual who thrives on keeping up to speed related to new technologies, specially AI and GenAI. We seek an experienced Change Management and Adoption Specialist who will lead and support change initiatives, ensuring stakeholders are engaged, trained, and equipped to adopt new processes, systems, and technologies.

The goal of this role is to minimize disruption, maximize benefits, and ensure a smooth transition to new ways of working.

• Lead the end-to-end design, development, and productionization of advanced machine learning and AI solutions, ensuring they address complex business challenges with measurable impact.
• Optimize and deploy state-of-the-art ML models and AI agents, leveraging modern frameworks (e.g., Lang Graph or similar) and best practices for scalability, reliability, and maintainability.
• Drive experimentation and continuous improvement cycles, including robust change management and iterative model refinement to maximize solution performance.
• Conduct and oversee comprehensive data analysis, preprocessing, and feature engineering, ensuring data quality and readiness for model development.
• Collaborate closely with data scientists, quantitative analysts, software engineers, data engineers, and domain experts to define requirements, set technical direction, and deliver high-impact AI applications.
• Mentor and guide junior team members, fostering a culture of technical excellence, innovation, and knowledge sharing.
• Document and communicate machine learning processes, methodologies, and results to both technical and non-technical stakeholders, ensuring transparency and reproducibility.
• Stay abreast of the latest advancements in AI/ML research and technology, proactively integrating relevant innovations into the team’s workflows and solutions.

• Minimum 5 years of professional software development experience, with a strong emphasis on AI/ML and advanced Python programming.
• Hands‑on expertise in building and deploying GenAI applications using Large Language Models (LLMs), Retrieval‑Augmented Generation (RAG), AI agents, and state‑of‑the‑art frameworks (e.g., Lang Chain, Lang Graph, OpenAI, Llama, Mistral, etc.).
• Deep understanding of transformer architectures, cross‑encoders, and experience with leading libraries such as Hugging Face Transformers, Tensor Flow, and PyTorch.
• Experience with prompt engineering, fine‑tuning, transfer learning, and model customization for LLMs and other advanced models.
• Strong foundation in data structures, algorithms, and software engineering principles, with a track record of delivering high‑quality, maintainable, and scalable code.
• Experience integrating and manipulating data using SQL and No
  
  SQL databases (e.g., Postgre
  
  SQL, Elasticsearch, Neo4j, or similar), and building data pipelines for AI/ML applications.
• Familiarity with Azure AI and machine learning services (or equivalent cloud platforms) for deploying, monitoring, and maintaining ML models in production environments.
• Industry experience in machine learning at scale, including model lifecycle management, monitoring, and optimization.
• Excellent problem‑solving skills and a passion for tackling complex challenges in AI/ML development and deployment.
• Proven ability to work collaboratively in cross‑functional teams, driving results in fast‑paced, innovative environments—preferably within banking or finance technology domains.
• Up‑to‑date knowledge of the latest advancements in LLMs, GenAI, and AI agent frameworks, with a commitment to continuous learning and innovation.

• Advanced knowledge of machine learning, deep learning, and data science concepts, including experience with statistical modeling and experimentation.
• Experience with modern front‑end frameworks or building AI‑powered user interfaces.
• Hands‑on experience with Snowflake, Postgre
  
  SQL, Neo4j, Elasticsearch, or other advanced database and data management platforms.
• Familiarity with MLOps tools and practices (e.g., MLflow, Kubeflow, DVC) for model versioning, deployment, and monitoring.

• Big Data Management
• Data Mining
• Data Science
• Deep Learning
• Machine Learning (ML)
• Predictive Analytics
• Programming Languages

• A comprehensive Total Rewards Program including bonuses and flexible benefits, competitive compensation, commissions, and stock where applicable.
• Leaders who support your development through coaching and managing opportunities.
• Ability to make a difference and lasting impact.
• Work in a dynamic, collaborative, progressive, and high‑performing team.
• A world‑class training program in financial services.
• Opportunities to do challenging work.
• Opportunities to take on progressively greater accountabilities.
• Opportunities to build close relationships with clients.
• Access to a variety of job opportunities across business and geographies.