Applied AI ML Lead - Data Scientist / Engineer - Commercial and Investment Bank

Join . Morgan as an AI/ML Data Scientist/Engineer, where you'll be at the forefront of innovation, designing and deploying cutting-edge AI solutions to optimize business processes. Work in a collaborative environment that values diversity and inclusion, and contribute to impactful projects that drive the future of finance.

As an AI/ML Data Scientist/Engineer within our dynamic team, you will be tasked with the development and implementation of machine learning models aimed at addressing intricate operational issues. Your role will involve collaborating with us to pinpoint automation possibilities and promote data-promoten decision-making. Together, we will ensure the scalability and reliability of AI/ML solutions.

Job Responsibilities:

Develop and implement machine learning models and algorithms to solve complex operational challenges. Design and deploy generative AI applications to automate and optimize business processes. Collaborate with stakeholders to understand business needs and translate them into technical solutions. Analyze large datasets to extract actionable insights and drive data-driven decision-making. Ensure the scalability and reliability of AI/ML solutions in a production environment. Stay up-to-date with the latest advancements in AI/ML technologies and integrate them into our operations. Mentor and guide junior team members in AI/ML best practices and methodologies.

Required qualifications, capabilities and skills:

. in Computer Science, Data Science, Machine Learning, or a related field. Proven experience in deploying AI/ML applications in a production environment, with skills in deploying models on AWS platforms such as SageMaker or Bedrock. Familiarity with MLOps practices, encompassing the full cycle from design, experimentation, deployment, to monitoring and maintenance of machine learning models. Strong expertise in machine learning frameworks such as TensorFlow, PyTorch, Pytorch Lightning, or Scikit-learn. Proficiency in programming languages such as Python. Proficiency in writing comprehensive test cases, with a strong emphasis on using testing frameworks such as pytest to ensure code quality and reliability. Experience with generative AI models, including GANs, VAEs, or transformers.  Solid understanding of data preprocessing, feature engineering, and model evaluation techniques. Familiarity with cloud platforms (AWS) and containerization technologies (Docker, Kubernetes, Amazon EKS). Excellent problem-solving skills and the ability to work independently and collaboratively. Strong communication skills to effectively convey complex technical concepts to non-technical stakeholders.

Preferred qualifications, capabilities and skills:

Experience in the financial services industry, particularly within investment banking operations. Experience in developing AI solutions using agentic frameworks. Experience fine-tuning SLMs with approaches like LoRA, QLoRA, and DoRA. Experience with prompt optimization frameworks such as AutoPrompt and DSPY to enhance the performance and effectiveness of prompt engineering. Familiarity with distributed computing systems, frameworks, and techniques like data sharding and DDP training. Experience with Diffusion models is a plus.

What We Offer:

Competitive salary and benefits package. Opportunities for professional growth and development. A collaborative and innovative work environment. The chance to work on impactful projects that drive the future of finance.