Applied AI ML Engineer-Vice President
Role details
Job location
Tech stack
Job description
As an Applied AI ML Engineer-Vice President in our Corporate & Investment Bank, you will be responsible for designing, developing, and deploying cutting-edge AI and machine learning solutions to enhance the efficiency and effectiveness of our operations. You will work closely with cross-functional teams to identify opportunities for automation and process improvement, utilizing your expertise in machine learning and generative AI., * 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.
Requirements
- Ph.D. with 2+ years experience or Masters with 4+ years experience in Computer Science, Data Science, Machine Learning, or a related field.
- 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.
- Expertise in machine learning frameworks such as TensorFlow, PyTorch, Pytorch lightening, 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.
Benefits & conditions
We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process.