Machine Learning Engineer – Financial Services
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Job Description
Machine Learning Engineer with a focus on Generative AI and financial services domain. Building demos and scalable architectures in a fast-paced environment with client interaction.
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 guidelines to ensure that 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 needs
- 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
Requirements:
- 4+ years of experience in IT industry, 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 with pre-sales activities 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 fundamentals (security, governance, etc.)
- Experience with cloud platforms (AWS, Google Cloud, Azure) or on-premises setups
- Ability to solve problems and handle shifting priorities with team support
- Experience with client-facing roles
- Ability to demonstrate ideas and solutions clearly and confidently
- 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
- Knowledge of other programming languages, such as Java or Go
- Experience with open-source projects or exposure to tools, such as Airflow or Spark
- Familiarity with containers (e.g., Docker) or orchestration tools (e.g., Kubernetes)
- Experience in the Banking and Financial Services domain
- Experience with prompt engineering or fine-tuning LLMs
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