Senior AWS Datalake AI/ML Engineer

Senior AI/ML Data Architect or Lead Machine Learning Engineer
Location: Cambridge, VT
Type: Contract 

 

 

 

Role Descriptions:

 

We are seeking a seasoned Data Science Operations Lead to spearhead our AI/ML and data science operations. This leadership role requires a hands-on technologist with 12+ years of experience who can drive the end-to-end lifecycle of data science and machine learning initiatives from ideation and experimentation through production deployment and ongoing operations at enterprise scale.

The ideal candidate combines deep technical expertise in AWS cloud (including AWS Data Lake architectures), Databricks, and big data processing with strong leadership and stakeholder management skills honed in large, multinational organizations operating in onshore/offshore delivery models. Experience with Generative AI is a strong differentiator.

Key Responsibilities

Leadership & Strategy

· Lead and manage a team of 6+ data scientists, ML engineers, and analytics professionals across onshore/offshore locations, providing technical mentorship and career guidance.

· Define and drive the data science operations strategy, roadmap, and best practices aligned with business objectives.

· Partner with senior business stakeholders, product owners, and cross-functional teams to identify high-impact AI/ML opportunities and translate them into actionable project plans.

· Establish and govern standards for model development, deployment, monitoring, and responsible AI adoption across the organization.

Hands-On Technical Delivery

· Architect and oversee scalable ML pipelines for data ingestion, feature engineering, model training, validation, and inference on AWS cloud and Databricks.

· Design and implement AWS Data Lake architectures and big data processing solutions for structured and unstructured data at petabyte scale using Spark, Databricks, and AWS-native services (S3, Lake Formation, EMR, Glue, SageMaker, Redshift, Athena).

· Lead the deployment of production ML systems including real-time inference APIs, batch prediction pipelines, and model-as-a-service architectures.

· Drive MLOps maturity — CI/CD for ML, automated model retraining, drift detection, A/B testing, and performance monitoring.