Senior Machine Learning Engineer

About the role

Senior Machine Learning Engineer

📍 Location: New York, NY, US

🏢 Industry: Financial services

💼 Work Setting: Hybrid

Are you a Machine Learning Engineer who enjoys building production-grade AI systems, scalable data pipelines, and cloud-native machine learning platforms?

We are seeking a Machine Learning Engineer (MLE) to design, build, deploy, monitor, and optimize machine learning solutions that solve real-world business challenges at scale. This role sits at the intersection of Machine Learning, Software Engineering, Data Engineering, MLOps, and Cloud Computing, partnering closely with Product, Data Science, and Engineering teams to deliver reliable and responsible AI solutions.

The ideal candidate combines expertise in Python, distributed computing, ML frameworks, cloud platforms, data pipelines, MLOps, CI/CD, and Responsible AI with strong software engineering fundamentals.

Key Responsibilities

Machine Learning Model Development

Design, build, and deploy machine learning models that solve complex business problems.

Collaborate with Product and Data Science teams to develop analytical and predictive solutions.

Translate business requirements into scalable ML applications.

Evaluate and improve model performance throughout the lifecycle.

Support production-grade AI and machine learning environments.

Areas of Focus

Predictive Modeling

Classification

Regression

Recommendation Systems

Intelligent Automation

Business Optimization

ML Architecture & Engineering

Design scalable machine learning architectures.

Select appropriate algorithms, data structures, and training approaches.

Evaluate trade-offs between accuracy, performance, cost, and scalability.

Build systems that support large-scale model training and inference.

Develop reusable machine learning components and services.

Key Areas

Feature Engineering

Model Selection

Hyperparameter Tuning

Dimensionality Reduction

Model Validation

Bias-Variance Optimization

MLOps & Model Lifecycle Management

Deploy machine learning models into production environments.

Establish monitoring, retraining, and maintenance processes.

Track model health and performance over time.

Implement automated model lifecycle management.

Respond to model drift and performance degradation.

Responsibilities

Model Deployment

Model Monitoring

Automated Retraining

Drift Detection

Performance Optimization

Production Support

Data Pipeline Engineering

Design and build scalable data pipelines that support machine learning workloads.

Develop reliable processes for:

Data Collection

Data Preparation

Feature Engineering

Training Data Generation

Inference Workflows

Improve data quality, reliability, and accessibility.

Support large-scale distributed processing environments.

Distributed Computing & Big Data Solutions

Build data-intensive applications using distributed computing technologies.

Develop scalable platforms capable of processing large datasets.

Optimize data and model training pipelines.

Support high-throughput machine learning workloads.

Improve operational efficiency through robust engineering practices.

Cloud Machine Learning Platforms

Deploy and operate machine learning workloads on cloud infrastructure.

Build cloud-native ML solutions that scale efficiently.

Optimize cost, performance, and reliability.

Support end-to-end ML platform modernization initiatives.

Cloud Environments

Amazon Web Services (AWS)

Microsoft Azure

Google Cloud Platform (GCP)

Software Engineering & Development

Develop clean, maintainable, and well-tested application code.

Follow software engineering best practices.

Create reusable services and libraries.

Participate in architecture discussions and engineering reviews.

Build resilient and scalable production systems.

CI/CD & Automation

Implement continuous integration and continuous deployment processes.

Automate:

Model Testing

Validation

Deployment

Monitoring

Release Processes

Improve deployment speed and reliability.

Support DevOps and MLOps best practices.

Responsible & Explainable AI

Ensure machine learning systems follow Responsible AI principles.

Build explainable and transparent models where appropriate.

Support governance, risk management, and compliance requirements.

Reduce vulnerabilities and operational risks associated with AI systems.

Promote ethical and trustworthy AI practices.

Agile Collaboration

Work within cross-functional Agile teams.

Collaborate closely with:

Data Scientists

Software Engineers

Product Managers

Data Engineers

Platform Engineers

Participate in sprint planning, code reviews, retrospectives, and continuous improvement activities.

Contribute to technical direction and innovation initiatives.

Qualifications

Education

Required

Bachelor's Degree in:

Computer Science

Engineering

Mathematics

Statistics

Data Science

Related Technical Discipline

Preferred

Master's Degree or PhD in:

Computer Science

Electrical Engineering

Mathematics

Artificial Intelligence

Machine Learning

Related Quantitative Field

Experience

Required

4+ years of programming experience using:

Python

Scala

Java

3+ years designing and building data-intensive distributed systems.

2+ years working with industry-standard machine learning frameworks.

1+ year productionizing and maintaining machine learning models.

Experience developing scalable machine learning applications.

Technical Skills

Machine Learning Frameworks

Required

Scikit-learn

PyTorch

TensorFlow

Spark ML

Dask

Programming Languages

Required

Python

Scala

Java

Data Engineering

Required

Data Pipelines

Feature Engineering

ETL / ELT

Data Transformation

Distributed Processing

Cloud Platforms

Preferred

AWS

Azure

Google Cloud Platform (GCP)

Areas

Cloud ML Services

Model Deployment

Scalable Infrastructure

Production Analytics

MLOps

Required

Model Deployment

Model Monitoring

Automated Testing

CI/CD

Drift Detection

Retraining Pipelines

Distributed Systems

Preferred

Distributed File Systems

Multi-Node Databases

Distributed Computing Frameworks

Big Data Platforms

Preferred Qualifications

Advanced Machine Learning Engineering

Experience scaling ML systems.

Experience optimizing production AI environments.

Experience evaluating machine learning pipeline performance.

Experience supporting enterprise-wide AI implementations.

Research & Innovation

Open Source ML Contributions

Published Research Papers

Machine Learning Proofs of Concept

AI Innovation Programs

AI Productivity Tools

Experience using advanced AI-assisted development tools.

Familiarity with modern AI engineering workflows.

Experience leveraging AI tools beyond basic code generation.

Professional Competencies

Machine Learning Engineering

Software Engineering

Problem Solving

Analytical Thinking

Systems Design

Collaboration

Communication

Innovation

Continuous Learning

Core Competencies

Machine Learning

MLOps

Python

Scala

Java

PyTorch

TensorFlow

Scikit-learn

Spark

Dask

Data Engineering

Distributed Computing

Cloud Computing

AWS

Azure

GCP

CI/CD

Feature Engineering

Model Deployment

Responsible AI

Explainable AI