Machine Learning Engineer (Financial Services domain)

We’re looking for a passionate Machine Learning Engineer to join our team and dive into the exciting world of Generative AI. You’ll work on cutting-edge demos and proof of concepts (POCs), turning bold ideas into reality. The ideal candidate brings solid Back-end engineering skills and a curiosity for Generative AI development. If you’re familiar with MLOps, large language models (LLMs), or concepts like Retrieval-Augmented Generation (RAG). Experience in the Financial Services domain, particularly Banking, will be a significant advantage.

You will be exposed to a variety of tasks, including:

• Develop Proof of Concepts (PoC). Test and validate new ideas
• Drive implementation, optimizing and transforming data solutions to improve business outcomes
• Help with offerings as a technical consultant

Requirements:

• 4+ years of experience in IT with at least 2-3 years of experience in machine learning
• Solid Back-end engineering skills, particularly with Python (e.g., Django, Flask, or FastAPI)
• Experience in pre-sales and opportunity processing
• Basic experience with databases or tools like vector databases (e.g., Pinecone, Weaviate, Faiss)
• Familiarity with AI frameworks such as TensorFlow, PyTorch, or Hugging Face
• Understanding of CI/CD pipelines
• Knowledge of RAG or AI application basics (security, governance, etc.)
• Experience with cloud platforms (AWS, Google Cloud, Azure) or on-premises setups
• Strong problem-solving skills and ability to handle shifting priorities with Support team
• Experience with client-facing roles
• Excellent presentation and demonstration skills
• Bachelor’s or Master’s degree in computer science, machine learning, artificial intelligence, or a related field
• Upper-Intermediate level of English

Would be a plus:

• Contributions to open-source projects or experience with tools like Airflow or Spark
• Familiarity with containers (e.g., Docker) or orchestration tools (e.g., Kubernetes)
• Experience in the Banking and Financial Services domain
• Exposure to prompt engineering or fine-tuning LLMs
• Knowledge of other languages like Java or Go

Responsibilities:

• Assist in building and testing Generative AI demos and POCs
• Support the design of simple, scalable architectures for Generative AI applications
• Work with team members to integrate AI components into larger systems
• Use MLOps practices to help automate parts of the model development process
• Follow guidance to ensure Generative AI applications are secure and meet basic governance standards
• Help deploy AI applications on cloud platforms or on-premises setups with team support
• Adapt to a fast-paced environment with evolving project requirements
• Keep up with AI trends and apply them to projects with guidance
• Advise clients. Understand their needs, analyze possible solutions, and present the best options