Machine Learning Engineer

About the Client

Our client is revolutionizing B2B payments by modernizing payment facilitation and settlement operations. As we continue to scale, we are investing in machine learning to drive operational efficiency, uncover new insights, and deliver smarter financial products.

Our AI/ML journey is just beginning—and we’re looking for a hands-on leader to help us build it from the ground up.

About the Role

We are seeking a Principal Applied Machine Learning Engineer to be the foundational hire responsible for establishing the company's machine learning capabilities. This is a high-impact, high-ownership role for someone who thrives in greenfield environments and is excited to design, build, and scale ML systems from scratch.

You will lead the development of ML infrastructure and pipelines, work on key use cases across operations and product, and drive best practices for MLOps, model deployment, and lifecycle management.

As a deeply technical and execution-focused individual, you will collaborate closely with business, engineering, and data stakeholders to translate strategic opportunities into deployable machine learning solutions. You'll also play a pivotal role in mentoring team members, embedding ML thinking across the organization, and evangelizing AI/ML adoption.

This is a rare opportunity to shape the architecture, culture, and future of machine learning.

Responsibilities

Design, build, and own end-to-end machine learning pipelines, including data ingestion, feature engineering, model training, deployment, and automated retraining.

Develop models for classification, regression, NLP, and LLM-based use cases, aligned to critical business needs in operations, payments, and product workflows.

Establish and maintain ML infrastructure, including CI/CD workflows for ML, model versioning, monitoring, and automated deployment.

Leverage AWS services—including SageMaker, Bedrock, Lambda, Comprehend, and Rekognition—to develop secure, scalable, and cost-effective ML solutions.

Set and implement best practices for the entire ML lifecycle, utilizing tools such as MLflow, SageMaker Pipelines, and feature stores to ensure experiment reproducibility, traceability, and governance.

Translate business requirements into technical designs in close collaboration with product, data, and engineering teams.

Define success metrics for ML initiatives, scope MVPs, and iterate based on feedback and performance. Act as a catalyst for AI/ML capability-building by mentoring team members, sharing best practices, and embedding ML literacy across the organization. Stay current with emerging ML trends and AWS innovations, and assess their potential for business applications

Integrate ML models into production environments, working closely with backend engineers to ensure seamless deployment into microservices or data pipelines.

Own the delivery of ML projects from ideation to monitoring, operating with autonomy and a strong bias for action.

Requirements

Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related technical field.

5+ years of experience in a Machine Learning Engineering or Applied ML role, with demonstrated impact in production environments.

Deep hands-on experience with AWS ML services, especially SageMaker and Bedrock.

Strong programming skills in Python (preferred), with additional experience in Java or Scala.

Expertise in traditional ML algorithms (e.g., XGBoost, Random Forests) as well as experience working with LLMs or foundation models

Demonstrated experience designing and deploying ML infrastructure and pipelines in cloud environments

Applied experience with MLflow or similar platforms for tracking experiments and managing models.

Solid understanding of model evaluation, feature engineering, hyperparameter tuning, and practical deployment constraints.

Strong interpersonal skills and ability to collaborate with cross-functional stakeholders to scope and deliver business-aligned ML solutions.

Self-starter with a strong sense of ownership and the ability to thrive in a greenfield environment.

Experience mentoring junior engineers or data scientists and fostering a collaborative, growth-oriented culture.

Preferred Qualifications

AWS Machine Learning Specialty certification or equivalent.

Experience with deep learning frameworks such as TensorFlow, PyTorch, or Keras.

Familiarity with big data tools like Spark, Kafka, or Hadoop.

Understanding of MLOps principles, including model monitoring, drift detection, and CI/CD for ML.

Exposure to fintech, payments, or regulated environments is a plus.

Benefits after conversion to full-time with our client

Competitive salary and benefits package.

Opportunity to work on cutting-edge ML projects.

Collaborative and innovative work environment.

Professional development and growth opportunities.