Applied AI/ML Engineer-Vice President

If you are looking for a game-changing career, working for one of the world's leading financial institutions, you’ve come to the right place.

As an Applied AIML Engineer, you provide expertise and engineering excellence as an integral part of an agile team to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Leverage your advanced technical capabilities and collaborate with colleagues across the organization to drive best-in-class outcomes across various technologies to support one or more of the firm’s portfolios.

Job responsibilities

• Co-Develop and implement LLM-based, 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 & Data Scientists 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 & LLMs and integrate them into our operations.
• Mentor and guide junior team members in coding & SDLC standards, AI/ML best practices and methodologies.

Required qualifications, capabilities, and skills

• Master’s or Bachelors in Computer Science, Data Science, Machine Learning, or a related field, with a focus on engineering.
• Excellent API design and engineering experience with proven usage of API python frameworks Quart, Flask or FastAPI
• Proficiency in Python & async programming, with a strong emphasis on writing comprehensive test cases using testing frameworks such as pytest to ensure code quality and reliability
• Expertise with Index & Vector DBs such as Opensearch./ElasticSearch
• Extensive experience in deploying AI/ML applications in a production environment, with skills in deploying models on AWS platforms such as SageMaker or Bedrock.
• Champion of MLOps practices, encompassing the full cycle from design, experimentation, deployment, to monitoring and maintenance of machine learning models.
• Experience with generative AI models, including GANs, VAEs, or transformers. Experience with Diffusion models is a plus.
• Solid understanding of data preprocessing, prompt engineering, feature engineering, and model evaluation techniques.
• Proficiency in AI coding tools and editors such as Cursor, Windsurf or CoPilot
• Familiarity in machine learning frameworks such as TensorFlow, PyTorch, PyTorch Lightning, or Scikit-learn.
• Familiarity with cloud platforms (AWS) and containerization technologies (Docker, Kubernetes, Amazon EKS, ECS).