MLOps Engineer

Insight Global; Glendale (CA); On-site; USD 150,000 - 190,000; Full time

Job summary

Insight Global in Glendale, CA is seeking a highly technical Data Engineer / ML Platform Engineer to design, build, and scale data and machine learning solutions from concept to production.

You will work with data scientists and stakeholders to transform data into actionable decisions, building scalable pipelines with Python, SQL, BigQuery, and ML tooling, while ensuring quality, observability, and cost efficiency.

Qualifications

• 5+ years of experience in Data Engineering, ML Engineering, MLOps, or related disciplines.
• Advanced Python development experience.
• Advanced SQL experience.
• Experience designing and supporting production data pipelines.
• Experience with cloud-native data platforms (GCP preferred, AWS or Azure accepted).
• Experience deploying and supporting production-grade machine learning or analytical solutions.
• Experience with source control and modern software engineering practices (Git/GitHub).
• Strong understanding of data modeling, pipeline architecture, and scalable system design.
• Google Cloud Platform experience, including BigQuery, Vertex AI, Cloud Run, Cloud Composer, and Pub/Sub.
• Experience with dbt.
• Experience with machine learning deployment and MLOps practices.
• Experience supporting recommendation systems, customer analytics, pricing solutions, experimentation platforms, or optimization models.
• Experience handling large-scale transactional, customer, web, or geospatial datasets.
• Exposure to LLMs, RAG frameworks, AI agents, vector databases, or generative AI applications.
• Experience with graph technologies such as Neo4j or similar platforms.
• Experience building internal platforms, reusable frameworks, or developer tooling.
• Experience working in highly collaborative environments where engineers and Data Scientists partner closely.

Responsibilities

• Data Engineering & Platform Development: Design, build, and maintain scalable data pipelines using Python, SQL, BigQuery, dbt, Airflow/Cloud Composer, and Pub/Sub.
• Develop reliable batch and real-time data processing solutions that support analytics, machine learning, and operational decision-making.
• Build reusable and maintainable data models that scale across multiple business domains.
• Improve data quality, monitoring, lineage, governance, and observability practices.
• Optimize large-scale data environments for performance, scalability, and cost efficiency.
• Partner with Data Science teams to create trusted datasets and production-ready data assets.
• Work with structured, semi-structured, and unstructured datasets including customer, operational, web, geospatial, and transactional data.
• ML Platform & MLOps Engineering: Productionize machine learning models and analytical solutions developed by Data Scientists.
• Develop and maintain model deployment frameworks within Google Cloud.
• Implement CI/CD pipelines, automated testing, monitoring, version control, and rollback strategies.
• Support model lifecycle management, inference workflows, and operational scalability.
• Build platforms and tooling that enable Data Scientists to move from experimentation to production more efficiently.
• Improve reliability and observability of AI and machine learning systems.
• Decision Sciences & AI Enablement: Support AI, LLM, and intelligent decision-support initiatives.
• Partner with Data Scientists to operationalize recommendation engines, customer intelligence models, pricing solutions, and optimization frameworks.
• Enable near real-time and event-driven analytics capabilities.
• Contribute to experimentation platforms, model explainability, and business-facing analytical products.
• Support future innovation involving generative AI, graph analytics, and emerging machine learning technologies.

Skills

Python development

SQL

Data pipelines

GCP experience

BigQuery

dbt

Airflow

Pub/Sub

MLOps

ML deployment

Git/GitHub

Data modeling

Pipeline architecture

Cloud platforms

Vertex AI

Cloud Composer

Neo4j

LLMs

Tools

dbt

Airflow

BigQuery

Pub/Sub

Vertex AI

Cloud Run

Cloud Composer

Neo4j

Job description

Job Description

We are looking for a highly technical Data Engineer / ML Platform Engineer who can design, build, and scale data and machine learning solutions from concept to production.

This role works closely with Data Scientists, Analytics leaders, and business stakeholders to transform data into actionable decision-making tools.

Success in this role requires someone who is comfortable wearing multiple hats, working through ambiguity, and taking ownership of solutions throughout their lifecycle.Data Engineering & Platform Development (60%)Design, build, and maintain scalable data pipelines using Python, SQL, BigQuery, dbt, Airflow/Cloud Composer, and Pub/Sub.Develop reliable batch and real-time data processing solutions that support analytics, machine learning, and operational decision-making.Build reusable and maintainable data models that scale across multiple business domains.Improve data quality, monitoring, lineage, governance, and observability practices.Optimize large-scale data environments for performance, scalability, and cost efficiency.Partner with Data Science teams to create trusted datasets and production-ready data assets.Work with structured, semi-structured, and unstructured datasets including customer, operational, web, geospatial, and transactional data.ML Platform & MLOps Engineering (25%)Productionize machine learning models and analytical solutions developed by Data Scientists.Develop and maintain model deployment frameworks within Google Cloud.Implement CI/CD pipelines, automated testing, monitoring, version control, and rollback strategies.Support model lifecycle management, inference workflows, and operational scalability.Build platforms and tooling that enable Data Scientists to move from experimentation to production more efficiently.Improve reliability and observability of AI and machine learning systems.Decision Sciences & AI Enablement (15%)Support AI, LLM, and intelligent decision-support initiatives.Partner with Data Scientists to operationalize recommendation engines, customer intelligence models, pricing solutions, and optimization frameworks.Enable near real-time and event-driven analytics capabilities.Contribute to experimentation platforms, model explainability, and business-facing analytical products.Support future innovation involving generative AI, graph analytics, and emerging machine learning technologies.

Skills and Requirements

5+ years of experience in Data Engineering, ML Engineering, MLOps, or related disciplines.Advanced Python development experience.Advanced SQL experience.Experience designing and supporting production data pipelines.Experience with cloud-native data platforms (GCP preferred, AWS or Azure accepted).Experience deploying and supporting production-grade machine learning or analytical solutions.Experience with source control and modern software engineering practices (Git/GitHub).Strong understanding of data modeling, pipeline architecture, and scalable system design.Demonstrated ability to lead technical solutions and make sound architectural decisions.Google Cloud Platform experience, including BigQuery, Vertex AI, Cloud Run, Cloud Composer, and Pub/Sub.Experience with dbt.Experience with machine learning deployment and MLOps practices.Experience supporting recommendation systems, customer analytics, pricing solutions, experimentation platforms, or optimization models.Experience handling large-scale transactional, customer, web, or geospatial datasets.Exposure to LLMs, RAG frameworks, AI agents, vector databases, or generative AI applications.Experience with graph technologies such as Neo4j or similar platforms.Experience building internal platforms, reusable frameworks, or developer tooling.Experience working in highly collaborative environments where engineers and Data Scientists partner closely.

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 employment opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment without regard to 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 the recruiting process, please send a request to [e-mail].