Remote ML Ops Engineer

Our Senior Data Science & ML Ops Engineer is a hands-on role focused on partnering with business leaders and technology teams to design, test, and deploy actionable machine learning solutions that drive measurable business outcomes. This role bridges data science, engineering, and operations—owning the full lifecycle from hypothesis and experimentation through production deployment and operationalization.

This position is centered on applied machine learning, using proven, off-the-shelf algorithms and scalable AWS services to rapidly validate ideas, embed models into business workflows, and ensure they are reliably running in production.

Business-Driven Experimentation & Model Ownership

Partner directly with business stakeholders to identify opportunities where data and machine learning can improve decisions, efficiency, or outcomes

Design experiments and hypotheses that can be validated quickly using available data and pragmatic modeling approaches

Select and apply out-of-the-box machine learning algorithms (e.g., classification, regression, forecasting, clustering, optimization)

Own models end-to-end—from data preparation and feature engineering through deployment, monitoring, and iteration based on real-world results



ML Implementation, Production & Operations

Deploy ML models into production using AWS-native tooling and integrate them into operational workflows and downstream systems

Implement ML training and inference pipelines on Amazon SageMaker, including pipelines, endpoints, model registry, and monitoring

Ensure production readiness through versioning, validation, rollback strategies, and performance monitoring

Monitor model performance (accuracy, drift, stability, business KPIs) and iterate based on real-world impact

Participate directly in diagnosis and resolution of production issues affecting data pipelines or ML workloads



Data Platform & Engineering Collaboration

Build and operate data ingestion and transformation pipelines across batch and event-driven workloads using AWS Glue, zero‑ETL integrations, Step Functions, EventBridge, and related services

Collaborate closely with IT, Security, and Platform Engineering teams to align with enterprise security, compliance, and operational standards

Use infrastructure as code (Terraform, CDK, or CloudFormation) to create repeatable, scalable environments



Data Governance, Lake Architecture & Operational Excellence

Own and operate S3-based data lake infrastructure, including Iceberg table formats, AWS Glue Data Catalog, and AWS Lake Formation

Implement and enforce data zone architecture (e.g., raw, curated, and consumption zones) to support governed data access and lifecycle management

Define and apply data access controls using Lake Formation permissions and IAM-aligned policies

Establish and maintain data governance practices, including schema management, schema evolution, and lineage tracking

Ensure data assets are discoverable, auditable, and secure through cataloging, metadata management, and access controls

Build end-to-end observability using CloudWatch, Datadog, pipeline SLAs, data quality checks, and model drift detection

Establish operational runbooks and support procedures for governed data and ML platforms



Cost-Effective, Scalable ML & Data Delivery

Apply cost-aware design when selecting data processing, training, and inference approaches

Optimize Glue, SageMaker, and storage usage to deliver value efficiently at scale

Continuously improve platform reliability, scalability, and cost efficiency as data and ML workloads grow

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.

5+ years in a professional data science role and 5 years of experience with machine learning pipelines, preferably in an AWS environment

Applied problem solver motivated by business outcomes and action

Strong business partner able to translate questions into testable hypotheses and executable solutions

Hands-on applied ML experience delivering models into production AWS environments

Proven experience operating governed data lakes and ML platforms at scale

Builder–operator mindset with strong CI/CD, observability, and incident response skills

Pragmatic practitioner who values reliability, adoption, governance, and impact over unnecessary complexity

Benefit packages for this role will start on the 1st day of employment and include medical, dental, and vision insurance, as well as HSA, FSA, and DCFSA account options, and 401k retirement account access with employer matching. Employees in this role are also entitled to paid sick leave and/or other paid time off as provided by applicable law.