Distinguished MLOps Engineer - Remote

What You Will Do:

• Collaborate with Acxiom's Architecture COE, product teams, data scientists, ML practitioners, devOps and analytics leaders to define requirements for ML platform design and modernization.
• Lead and direct junior engineers across US, Europe and Asia to enable the design and development of a modernized MLOps platform
• Contribute to the co-development of a comprehensive architecture for migrating existing capabilities to a modern ML infrastructure.
• Develop scalable and flexible hyperscale ML systems for batch training and inference, as well as real-time/near real-time workloads.
• Lead end-to-end solution design, encompassing assessment, roadmap creation, detailed technical design, and cloud migration execution.
• Develop reusable patterns, frameworks, and accelerators to facilitate repeatable and successful implementations.
• Align internal stakeholders on architectural decisions, fostering consensus for scalable, secure, and performant designs.
• Lead technical workshops, solution deep dives, and proof-of-value (POV) pilots to validate architectural feasibility and ensure alignment.
• Design and oversee modern MLOps pipelines, model management, governance, and security architectures.
• Establish governance frameworks and decision criteria for AI/ML and GenAI projects, ensuring adherence to industry standards, regulatory requirements, Responsible AI principles, and Acxiom/IPG architectural guidelines.
• Create and maintain reference ML architectures, patterns, and best practices for the AI/ML lifecycle and its integration within Acxiom's enterprise ecosystem (in partnership with Architecture COE).
• Define standards and guardrails for optimal execution of large inference workloads, emphasizing performance and cost efficiency.
• Leverage strong expertise in DevOps, CI/CD, and FinOps principles for cost optimization.
• Develop comprehensive migration plans for transitioning to Databricks, Snowflake, and other cloud ecosystems.
• Champion data sharing and clean room strategies to unlock the value of partner and third-party data collaboration.
• Stay abreast of evolving data and cloud technologies to provide clients with future-ready solutions.
• Mentor junior MLOps engineers across the organization to foster expertise and amplify impact.

What You Will Have:

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Information Systems, or a related field.
• 15+ years of ML platform engineering experience, including 12+ years focused on ML platform architecture, cloud modernization, and building large-scale ML platforms.
• Proven track record of designing and delivering hyperscale ML platforms across AWS, Azure, and GCP.
• 10+ years of experience optimizing Spark-based ML inference workloads and associated performance tuning.
• Proven Ability to develop large case ML solutions using H20, SparkML, scikit-learn and other ML tools.
• Demonstrated expertise in implementing MLOps pipelines and solutions using lower-level programming languages such as C/C++ or Java for optimal performance.
• Demonstrated experience in ML workload migration projects (e.g., Teradata, Hadoop, Oracle to Databricks/Snowflake).
• Deep understanding of machine learning modeling, MLOps, and model governance across marketing analytics use cases.
• Solid understanding of modern ML platforms, architectures, and MLOps frameworks (e.g., Mosai AI, CortexAI, Sagemaker, Vertex AI, Kubeflow, Airflow, MLflow).
• Minimum of 2 years of experience with GenAI, including technical familiarity with at least two of the following: OpenAI API, Bedrock API, Vertex API, LangGraph, or other agentic frameworks.
• Exceptional attention to detail and proven ability to manage multiple competing priorities simultaneously.
• 8-10+ years of architecting solutions using Databricks, with strong experience using Mosaic AI, Unity Catalog, MLflow, workflow orchestration, and other Databricks native MLOps capabilities
• Experience with MLOps and orchestration tools such as Airflow, Kubeflow, DAGster, Optuna, or MLflow.
• Strong CI/CD experience using tools like Terraform, Jenkins, and CloudFormation templates.
• Strong leadership, communication, and stakeholder engagement skills.
• Demonstrated ability to lead enterprise solutioning engagements and gain cross-functional alignment.
• Experience with data security and compliance controls, including data security modes, encryption, auditing, and access controls.
• Familiarity with cost optimization and performance tuning best practices in cloud and ML environments.

What Will Set You Apart:

• Databricks Certifications (e.g., Databricks Certified ML Professional).
• Snowflake certifications (e.g., SnowPro Core or Advanced Architect) and cloud platform certifications (AWS, Azure, GCP).
• Experience with industry use cases in marketing analytics, modeled propensities, or pre-built segmentation systems
• Experience with model and data security best practices, including access control, encryption, and compliance frameworks.

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Attention Colorado, California, Connecticut, Maryland, Nevada, New Jersey, New York City, Ohio, Rhode Island, and Washington Applicants: This position is not located in the aforementionedlocationsbut applications for remote work may be considered. For information about this role under state or local equal pay or pay transparency laws, please contactrecruit@acxiom.com.