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 Applied AI/ML Engineer in an agile team, 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 including Retrieval-Augmented Generation (RAG) systems, agentic AI frameworks, and multi-agent orchestration solutions to automate and optimize business processes
• Build and optimize AI agent architectures with tool-calling capabilities, reasoning chains, and autonomous decision-making workflows
• 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 using infrastructure as code practices
• Design and maintain cloud infrastructure using Terraform to support AI/ML workloads at scale
• Stay up-to-date with the latest advancements in AI/ML technologies, LLMs, and generative AI patterns, 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 Bachelor's degree 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 such as 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
• Hands-on experience with generative AI technologies including RAG (Retrieval-Augmented Generation) architectures, prompt engineering, and LLM fine-tuning techniques
• Experience building agentic AI systems, including agent frameworks (LangChain, LangGraph, AutoGen, CrewAI), tool integration, and multi-agent coordination
• Expertise with Index & Vector DBs such as OpenSearch, ElasticSearch, Pinecone, or Chroma for semantic search and retrieval applications and Experience in deploying AI/ML applications in a production environment, with skills in deploying models on AWS platforms such as SageMaker, Bedrock, or Lambda
• Champion of MLOps practices, encompassing the full cycle from design, experimentation, deployment, to monitoring and maintenance of machine learning models
• Solid understanding of data preprocessing, prompt engineering, few-shot learning, fine-tuning strategies, feature engineering, and model evaluation techniques
• Proficiency in AI coding tools and editors such as Cursor, Windsurf, or GitHub Copilot and Familiarity with 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)
• Experience with infrastructure as code using Terraform to provision and manage AWS resources for AI/ML workloads

Preferred qualifications, capabilities, and skills

• Working knowledge of Java programming for integration with enterprise systems and microservices architectures
• Expertise in cloud storage such as RDS and S3, and computing frameworks like AWS Glue or EMR
• Advanced Terraform skills including module development, state management, and CI/CD integration for infrastructure deployment
• Experience with AWS services such as Lambda, Step Functions, EventBridge, API Gateway, and CloudWatch for building serverless AI applications
• Knowledge of LLM evaluation frameworks, guardrails implementation, and responsible AI practices
• Experience with streaming data processing and real-time inference pipeline
• Excellent problem-solving skills and the ability to work independently and collaboratively