W2 – 10+ Senior Lead Machine Learning Engineer (MLOps & Data Platform)

Prohires; United States; Remote; USD 180,000 - 240,000; Full time

Job summary

Prohires is seeking a Senior Lead Machine Learning Engineer to steer ML data engineering, MLOps, and enterprise data platform initiatives in a remote US setting. You will collaborate with data scientists to develop models and ensure scalable ML pipelines and API-driven scoring across the data stack.

Ideal candidates bring 5+ years in ML/AI, hands-on experience with Databricks, Python, SQL, FastAPI, Docker, Kubernetes, and cloud platforms (Azure, AWS, GCP).

Qualifications

• BS in CS/engineering or related field.
• 5+ years AI/ML engineering experience.
• Strong data science foundation with 5+ years in the field.
• Able to work in a team and communicate effectively.
• Proficient with SQL, Python, R, Snowflake.

Responsibilities

• Prepare ML datasets and scoring processes.
• Maintain real-time and snapshot data sources across the company.
• Collaborate with Data Scientists to develop and implement ML models.
• Support Enterprise Data Platform strategy for ML predictions via APIs and data warehouses.
• Automate ML Data Engineering workflows with monitoring and alerts.
• Tune performance and optimize queries.
• Support Data Lifecycle Management in the ML team.
• Stay current with tech across databases, data lakes, warehouses.
• Work with Analytics to follow architecture, design & deployment processes.

Skills

ML experience

Databricks Unity Catalog

ML pipelines

Python

SQL

FastAPI

Snowflake

Docker

Kubernetes

Communication

Education

Bachelor’s degree in CS/Engineering

5 years AI/ML experience

Tools

Databricks

Azure ML Studio

AWS

GCP

Snowflake

Job description

Senior Lead Machine Learning Engineer (MLOps & Data Platform)

REMOTE

Duties: What you will do in this role:

• · Facilitate and participate in the preparation of ML data sets and scoring processes
• · Develop and maintain a knowledge of real-time and snapshotted data sources from across the company, as well as Analytics team members responsible for various sources
• · Work with the Data Scientists to develop and implement key machine learning models
• · Develop and support the ML portion of the Enterprise Data Platform strategy to maximize the distribution of ML predictions and recommendations, including API-based ML scoring and data warehouse-based access
• · Incorporate workflows into ML Data Engineering processes, to automate, monitor, and produce alerts
• · Implement performance tuning and query optimization
• · Act as a resource to the Machine Learning team with regards to Data Lifecycle Management
• · Keep pace with latest technology across various databases, data lakes, warehouses etc.
• · Collaborate with Analytics team members to follow all documented architecture, design & deployment processes to ensure compliance with policies
• · Design and develop scalable, reusable data models

Skills: To be successful in this role you have:

• · Experience in Machine Learning, Data Engineering, ML Infrastructure, or related fields, with strong foundations in probability, statistics, and machine learning.
• · Hands-on experience with Databricks, including Unity Catalog, and cloud ML platforms such as Azure ML Studio, AWS, or Google Cloud Platform.
• · Experience building and scaling end-to-end ML systems, ETL/ELT pipelines, and data workflows using Python, NumPy, Pandas, and SQL.
• · Experience building scalable APIs and microservices using FastAPI, with Snowflake or equivalent data warehouse technologies.
• · Experience deploying production workloads using Docker and Kubernetes, with a strong understanding of scalable, reliable software engineering practices.
• · Bachelor’s degree in Computer Science, Computer Engineering, Mathematics, Statistics, or equivalent field, with strong communication and cross-functional collaboration skills.

Education: Skills & Background:

• · Bachelor’s degree in a technical field such as computer science, computer engineering or related field required
• · 5 years of experience as AI/ML Engineer
• · Deep understanding of data science, including 5 years of experience in the space
• · Ability to work as part of a team, as well as work independently or with minimal direction
• · Excellent written, presentation, and verbal communication skills
• · Collaborate with data architects, modelers and IT team members on project goals
• · Strong knowledge of SQL, Python, R, Snowflake