Tech Lead (Machine Learning) - Finance (ID: 2037)

Truelogic is a leading provider of nearshore staff augmentation services, located in New York. Our team of 500 tech talents is driving digital disruption from Latin America to the top projects in U.S. companies. Truelogic has been helping companies of all sizes to achieve their digital transformation goals.

Would you like to make innovation happen? Have you ever dreamed of building Products that impact millions of users? Nice! Then we have a seat for you on our team!


What are you going to do?

You will have the opportunity to work in a forward-thinking and growth-oriented environment at a finance company that is a top national mortgage lender with over 4 million lifetime customers.

Occupy a unique position in the market, the project seeks a highly skilled Senior Software Engineer to architect, develop, and refine cutting-edge applications, machine learning models, integrations, and their supporting infrastructure. The selected candidate will be a key player in projects that leverage data science, code, data, and innovative technology to deliver actionable business insights. This role involves evaluating business requirements, designing robust solutions, and guiding junior developers through implementation.

  • Technical Leadership: Provide expert consultation on complex projects, demonstrating a deep understanding of complex code and machine learning concepts.
  • Solution Design: Define product requirements and create high-level architectural specifications for applications, ensuring feasibility, functionality, and seamless integration with existing systems and platforms.
  • Data Expertise: Collaborate with other teams to design, model, and optimize data pipelines for efficient model training and inference.


What will help you succeed

  • 3+ years of hands-on experience in software development and deployment.
  • 1+ years of experience in prompt engineering.
  • Programming: Extensive experience with Python and strong object-oriented programming (OOP) skills.
  • Machine Learning: Deep understanding of machine learning methodologies, including supervised, unsupervised, and reinforcement learning. Proficiency with Python machine learning libraries (scikit-learn, numpy, pandas, nltk).
  • Cloud Infrastructure: Significant experience with the AWS stack, including serverless architecture and cloud-based data warehousing solutions (e.g., Redshift).
  • Data Management: Experience with NoSQL databases and data manipulation techniques.
  • Agile Development: Familiarity with agile methodologies like SCRUM and LEAN.
  • MLOps: Experience with tools and practices for managing the machine learning lifecycle, including model versioning, deployment, monitoring, and retraining.