Machine Learning Engineer

Machine Learning Engineer

Location: New York City, NY

Industry: Financial Services

Work Setting: Hybrid

Are you passionate about building scalable machine learning platforms, deploying production-grade AI solutions, and leveraging cutting-edge technologies to solve complex business challenges through data-driven innovation?

We are seeking a Machine Learning Engineer to design, develop, deploy, and optimize machine learning solutions that power advanced analytics and intelligent applications at scale. This role combines machine learning engineering, software development, cloud architecture, MLOps, and data engineering to deliver impactful AI solutions that drive business value and accelerate digital transformation.

Key Responsibilities

Machine Learning Solution Development

Design, build, and deploy machine learning models and intelligent applications that address real-world business challenges.

Collaborate with data scientists, product managers, and engineering teams to transform business requirements into scalable ML solutions.

Develop reusable machine learning components and frameworks that support enterprise AI initiatives.

Apply advanced modeling techniques to improve prediction accuracy, automation, and decision-making capabilities.

Evaluate, test, and continuously improve machine learning performance across multiple use cases.

Machine Learning Engineering \& MLOps

Build and maintain production-grade machine learning pipelines and infrastructure.

Automate model training, validation, deployment, monitoring, and retraining processes.

Implement MLOps best practices that improve reliability, scalability, and operational efficiency.

Develop frameworks for model governance, versioning, monitoring, and lifecycle management.

Ensure machine learning systems remain accurate, resilient, and maintainable in production environments.

Data Engineering \& Pipeline Development

Design and optimize large-scale data pipelines supporting machine learning workflows.

Process, transform, and prepare structured and unstructured datasets for model development.

Build efficient data ingestion, feature engineering, and model training pipelines.

Leverage distributed computing technologies to support large-scale data processing requirements.

Ensure data quality, integrity, and availability throughout the machine learning lifecycle.

Cloud \& Infrastructure Engineering

Develop cloud-native machine learning solutions within scalable infrastructure environments.

Deploy and manage machine learning workloads using containerized and orchestration technologies.

Optimize cloud resources to maximize performance, reliability, and cost efficiency.

Support model serving, inference, and distributed training environments.

Collaborate with infrastructure teams to build reliable AI platforms and services.

Model Training, Evaluation \& Optimization

Select appropriate machine learning algorithms, architectures, and modeling approaches.

Conduct feature engineering, hyperparameter tuning, and model optimization activities.

Evaluate model performance using established metrics and validation methodologies.

Diagnose and resolve issues such as underfitting, overfitting, bias, and performance degradation.

Continuously improve model accuracy, scalability, and operational effectiveness.

Production Operations \& Monitoring

Monitor deployed machine learning models and applications in production environments.

Perform model retraining, performance tuning, and operational maintenance activities.

Investigate production issues and implement corrective actions to ensure system reliability.

Establish alerting, monitoring, and observability capabilities for AI solutions.

Support incident response and operational excellence initiatives.

Software Engineering Excellence

Develop high-quality, maintainable, and scalable software supporting machine learning systems.

Follow software engineering best practices including version control, testing, code reviews, and CI/CD.

Build automated testing frameworks for machine learning applications and pipelines.

Improve deployment automation and release reliability.

Contribute to a culture of engineering excellence and continuous improvement.

Responsible AI \& Governance

Ensure machine learning solutions adhere to responsible AI principles and governance standards.

Implement controls supporting model explainability, fairness, transparency, and compliance.

Evaluate and mitigate risks associated with AI and machine learning implementations.

Develop secure and well-governed ML systems that align with organizational policies.

Support auditability, monitoring, and model oversight requirements.

Cross-Functional Collaboration

Partner with product, engineering, data science, operations, and business teams.

Translate business challenges into practical machine learning solutions.

Communicate technical concepts and model outcomes to both technical and non-technical audiences.

Contribute to strategic AI initiatives and innovation programs.

Support knowledge sharing and best practices across teams.

Required Qualifications

Bachelor’s degree in Computer Science, Machine Learning, Data Science, Mathematics, Statistics, Engineering, or a related quantitative field.

4\+ years of software development experience using programming languages such as Python, Java, C\+\+, Golang, or similar technologies.

4\+ years of machine learning experience using modern machine learning frameworks and libraries.

Experience working with large-scale distributed data processing environments.

Experience deploying and supporting machine learning applications in production cloud environments.

Strong understanding of machine learning model development, deployment, and operationalization.

Technical Skills

Machine Learning Engineering

MLOps \& Model Lifecycle Management

Python, Java, Scala, or Golang

Deep Learning \& Neural Networks

PyTorch

TensorFlow

Scikit-learn

Pandas \& NumPy

Feature Engineering

Distributed Computing

Data Pipeline Development

Cloud Platforms (AWS, Azure, GCP)

Kubernetes \& Containers

CI/CD \& Automation

Software Engineering Best Practices

Preferred Qualifications

Master's or PhD in Computer Science, Artificial Intelligence, Machine Learning, Mathematics, Engineering, or a related discipline.

Experience optimizing machine learning infrastructure and model performance.

Experience designing and scaling production-ready ML platforms and services.

Experience leading technical initiatives or mentoring engineering teams.

Knowledge of advanced model architectures including transformers, CNNs, RNNs, LSTMs, and reinforcement learning models.

Experience contributing to research, publications, or innovative machine learning projects.