Machine Learning Engineer

Machine Learning Engineer

📍 Location: New York, NY, US

🏢 Industry: Financial Services

💼 Work Setting: On-site

Are you passionate about building scalable machine learning solutions, deploying AI models into production, and solving complex business problems through advanced analytics and cloud technologies? We are seeking a Machine Learning Engineer to design, develop, and operationalize machine learning systems that drive impactful business outcomes and support data-driven innovation.

In this role, you will work closely with data scientists, software engineers, platform teams, and business stakeholders to develop end-to-end machine learning solutions. You will be responsible for building production-ready ML models, creating data pipelines, optimizing infrastructure, and ensuring reliable model deployment and monitoring in cloud environments.

Key Responsibilities

Machine Learning Model Development

• Design, develop, and deploy machine learning models that address complex business and operational challenges.
• Translate business requirements into scalable AI and machine learning solutions.
• Evaluate, train, tune, and optimize machine learning algorithms for production use.
• Implement best practices throughout the model development lifecycle.

Production ML Engineering

• Deploy machine learning models into production environments and ensure operational reliability.
• Build frameworks and services that support scalable model inference and performance.
• Monitor production models and proactively address performance, accuracy, and stability concerns.
• Support model retraining, versioning, and lifecycle management activities.

Data Pipeline & Platform Development

• Design and maintain robust data pipelines that support machine learning workflows.
• Develop automated processes for data ingestion, transformation, feature engineering, and model training.
• Ensure data quality, consistency, and availability across machine learning systems.
• Optimize data processing workflows for efficiency and scalability.

Cloud & Infrastructure Engineering

• Build and maintain machine learning infrastructure within cloud environments.
• Leverage cloud-native services and architectures to support model training and deployment.
• Collaborate with platform engineering teams to improve scalability, reliability, and operational efficiency.
• Support infrastructure automation, monitoring, and deployment processes.

Distributed Systems & Scalability

• Design solutions capable of handling large-scale datasets and distributed workloads.
• Optimize machine learning applications for performance, availability, and fault tolerance.
• Support scalable architectures that accommodate growing data and business needs.
• Contribute to platform modernization and performance improvement initiatives.

Cross-Functional Collaboration

• Partner with data scientists, software engineers, product managers, and business stakeholders.
• Support the transition of models from research and experimentation into production systems.
• Participate in design reviews, architecture discussions, and strategic planning activities.
• Communicate technical concepts and project status effectively across teams.

Monitoring, Optimization & Continuous Improvement

• Establish monitoring frameworks for model health, performance, and business impact.
• Analyze model results and recommend enhancements that improve accuracy and effectiveness.
• Identify opportunities to improve automation, scalability, and development efficiency.
• Stay informed on emerging machine learning technologies, frameworks, and best practices.

Required Qualifications

• Bachelor's degree in:
• Computer Science
• Data Science
• Engineering
• Mathematics
• Statistics
• Related quantitative discipline
• 3+ years of software development experience using:
• Python
• Java
• Golang
• C++
• Similar programming languages
• 2+ years of hands-on experience with machine learning frameworks.
• 1+ year of experience deploying and supporting production machine learning solutions.
• Strong understanding of machine learning algorithms, model evaluation, and deployment methodologies.
• Experience working with cloud platforms and modern software engineering practices.
• Strong analytical, problem-solving, and communication skills.

Technical Skills

• Machine Learning
• Python
• Java
• Golang
• C++
• TensorFlow
• PyTorch
• Cloud Computing
• Data Pipelines
• Model Deployment
• Distributed Systems
• MLOps
• Feature Engineering
• Model Monitoring
• Data Engineering
• API Development
• Software Engineering
• CI/CD
• Cloud Infrastructure

Preferred Qualifications

• Experience building end-to-end machine learning platforms.
• Knowledge of MLOps, model governance, and automated deployment practices.
• Experience working with large-scale distributed data environments.
• Familiarity with containerization and cloud-native technologies.
• Experience supporting AI-driven products or customer-facing applications.
• Understanding of software architecture and scalable system design principles.