You are looking for a game-changing career, working for one of the world's leading financial institutions. As an Applied AI/ML 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 firms portfolios.
Job responsibilities include:
- 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.
Qualifications include:
- Masters 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.
- 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).
Preferred qualifications include expertise in cloud storage such as RDS and S3, problem-solving skills, communication skills, and project leadership experience.
