Machine Learning Engineer (Financial Services domain)

Remote: 
Full Remote
Contract: 
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Offer summary

Qualifications:

Bachelor's or Master's degree in computer science, machine learning, artificial intelligence, or a related field., 4+ years of IT experience with 2-3 years in machine learning and solid Back-end engineering skills, particularly in Python., Familiarity with AI frameworks like TensorFlow or PyTorch and understanding of CI/CD pipelines., Experience with cloud platforms and strong problem-solving skills..

Key responsibilities:

  • Develop and test Generative AI demos and Proof of Concepts (PoCs).
  • Drive implementation and optimize data solutions to improve business outcomes.
  • Advise clients by understanding their needs and presenting the best AI solutions.
  • Use MLOps practices to automate model development and ensure application security.

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Sigma Software Group Large https://www.sigma.software
1001 - 5000 Employees
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Job description

Company Description

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 Banking and Financial Services domain 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  

Job Description
  • 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 

Qualifications
  • 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 

Required profile

Experience

Spoken language(s):
English
Check out the description to know which languages are mandatory.

Other Skills

  • Problem Solving
  • Adaptability

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