AI/ML Engineer
About the role We are seeking a talented AI/ML Engineer with a minimum of 3 years of hands-on experience in the AI/ML domain. This exciting opportunity, based in India and reporting to the Software Development Manager, is your chance to build AI solutions from the ground up, working alongside the largest global clients in our industry.The ideal candidate will have a robust background in developing and deploying advanced machine learning models and generative AI (GenAI) solutions. You will play a pivotal role in integrating cutting-edge AI/ML capabilities—ranging from GenAI, Agents, ML Models, and Prompting Techniques to OCR, Vector Databases, and Retrieval Augmented Generation (RAG)—into our platform. Experience with AWS infrastructure, including AWS SageMaker and Bedrock, is considered a plus. More importantly, we’re looking for some who is eager to experiment, innovate, and delivery impactful AI-driven solutions. What you'll do Lead AI Innovation in Fintech: Design, develop, and deploy advanced AI/ML solutions that power the next generation of financial technology. Implement GenAI, agent-based systems, and sophisticated ML models to enhance our platform capabilities. Own the Full AI Lifecycle: Design and implement robust data models that support AI/ML initiatives. Architect data pipelines to ensure seamless data integration and processing as part of the solution. o Collaborate with cross-functional teams to integrate AI/ML functionalities into our multi-product, multi-issuer platform. Develop scalable machine learning pipelines and data processing workflows. Scale AI in the Cloud: Build, test, and optimize AI models on various cloud platforms; AWS experience (including SageMaker and Bedrock) is a bonus. Ensure robust deployment practices and maintain the performance and scalability of AI systems. Specialized Projects: Architect and implement an Agentic Framework tailored specifically for the needs of Financial Advisors, enabling autonomous reasoning, planning, and execution across complex financial [JP1] scenarios. Develop and enhance OCR capabilities and integrate these with vector databases. Utilize Retrieval Augmented Generation (RAG) techniques to improve data retrieval and decision-making processes. MLOps Integration (Plus): Champion MLOps best practices to streamline the continuous integration, delivery, and deployment of machine learning models. Collaborate with DevOps teams to optimize and monitor production-level AI/ML solutions. Be a Thought Leader: Provide technical guidance and mentorship to team members. Engage in knowledge-sharing sessions to drive continuous improvement across the team. Stay abreast of the latest advancements in AI/ML research, tools, and best practices. Experiment with novel prompting techniques and refine model architectures for improved outcomes.
Qualifications
Experience: Minimum 3 years of professional experience in AI/ML engineering or a related field. Must have hands-on experience working on a commercial product that is already in production. Technical Expertise: In-depth knowledge of GenAI, agent-based systems, ML models, and prompting techniques. Practical experience with OCR technologies, vector databases, and Retrieval Augmented Generation (RAG). Proficient in programming languages such as Python and familiar with machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn). Cloud Experience: Experience with cloud platforms is beneficial; AWS experience (specifically with AWS SageMaker and Bedrock) is a plus but not required. Soft Skills: Strong problem-solving abilities. Excellent communication skills and a collaborative mindset. Ability to thrive in a fast-paced, innovative environment. Education: Advanced degree (Master’s or PhD) in Computer Science, Data Science, Machine Learning, or a related discipline. Financial Technology Exposure: Experience in the financial technology sector, particularly with structured products or annuities. Additional Technical Skills: Familiarity with containerization (Docker, Kubernetes) and CI/CD pipelines. Experience with DevOps best practices and contributing to open-source projects. #LI-Remote