Acxiom
Acxiom is seeking a highly experienced Senior Director - MLOps Engineering to design and develop a scalable MLOps platform to support Acxiom’s modeled data product builds. This role requires deep knowledge of cutting-edge AI/ML and MLOps technologies and a passion for building robust platforms that enable modeled product development at scale. As a thought leader, you will drive modernization efforts and shape the future of ML platforms to enable advanced marketing analytics and effective customer engagement strategies.
You will collaborate with Senior architects, Data Scientists, ML practitioners and DevOps engineers across Acxiom to evaluate existing MLOps tools and technologies, define future-state architecture, and implement scalable, cloud-native solutions using platforms such as Databricks, Snowflake, and other enterprise services on AWS, GCP, and Azure. This role offers flexibility to be located almost anywhere within the U.S.
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 the 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 hyperscale ML systems for batch training and inference, as well as real-time/near real-time workloads. Lead end-to-end solution design, including 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 POV pilots to validate architectural feasibility and 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 within Acxiom's enterprise ecosystem (in partnership with Architecture COE). Define standards and guardrails for large inference workloads, emphasizing performance and cost efficiency. Leverage DevOps, CI/CD, and FinOps principles for cost optimization. Develop migration plans for transitioning to Databricks, Snowflake, and other cloud ecosystems. Champion data sharing and clean room strategies to unlock data collaboration with partners and third parties. Stay abreast of evolving data and cloud technologies to provide 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 performance tuning. Proven ability to develop large-scale ML solutions using H2O, SparkML, scikit-learn and other ML tools. Demonstrated expertise in implementing MLOps pipelines and solutions using languages such as C/C++ or Java for optimal performance. Experience in ML workload migration projects (e.g., Teradata, Hadoop, Oracle to Databricks/Snowflake). Deep understanding of ML 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 GenAI experience, with familiarity in at least two of: OpenAI API, Bedrock API, Vertex API, LangGraph, or other agentic frameworks. Strong leadership, communication, and stakeholder engagement skills. 8-10+ years of architecting solutions using Databricks, with experience including Mosaic AI, Unity Catalog, MLflow, and Databricks native MLOps capabilities. Experience with MLOps and orchestration tools such as Airflow, Kubeflow, Dagster, Optuna, or MLflow. CI/CD experience using Terraform, Jenkins, and CloudFormation templates. Experience leading enterprise solutioning engagements and 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 in cloud and ML environments. What Will Set You Apart
Databricks certifications (e.g., Databricks Certified ML Professional). Snowflake certifications (e.g., SnowPro Core) and cloud platform certifications (AWS, Azure, GCP). Experience with marketing analytics, modeled propensities, or pre-built segmentation systems. Model and data security best practices, including access control, encryption, and compliance frameworks. Primary Location:
Homebased - Conway, Arkansas We are an equal opportunity employer and do not discriminate in recruiting, hiring, training, promotion or other employment decisions because of race, color, sex, age, religion, national origin, disability, veteran status, or other protected status. Reasonable accommodations are available for applicants with disabilities.
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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 the 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 hyperscale ML systems for batch training and inference, as well as real-time/near real-time workloads. Lead end-to-end solution design, including 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 POV pilots to validate architectural feasibility and 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 within Acxiom's enterprise ecosystem (in partnership with Architecture COE). Define standards and guardrails for large inference workloads, emphasizing performance and cost efficiency. Leverage DevOps, CI/CD, and FinOps principles for cost optimization. Develop migration plans for transitioning to Databricks, Snowflake, and other cloud ecosystems. Champion data sharing and clean room strategies to unlock data collaboration with partners and third parties. Stay abreast of evolving data and cloud technologies to provide 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 performance tuning. Proven ability to develop large-scale ML solutions using H2O, SparkML, scikit-learn and other ML tools. Demonstrated expertise in implementing MLOps pipelines and solutions using languages such as C/C++ or Java for optimal performance. Experience in ML workload migration projects (e.g., Teradata, Hadoop, Oracle to Databricks/Snowflake). Deep understanding of ML 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 GenAI experience, with familiarity in at least two of: OpenAI API, Bedrock API, Vertex API, LangGraph, or other agentic frameworks. Strong leadership, communication, and stakeholder engagement skills. 8-10+ years of architecting solutions using Databricks, with experience including Mosaic AI, Unity Catalog, MLflow, and Databricks native MLOps capabilities. Experience with MLOps and orchestration tools such as Airflow, Kubeflow, Dagster, Optuna, or MLflow. CI/CD experience using Terraform, Jenkins, and CloudFormation templates. Experience leading enterprise solutioning engagements and 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 in cloud and ML environments. What Will Set You Apart
Databricks certifications (e.g., Databricks Certified ML Professional). Snowflake certifications (e.g., SnowPro Core) and cloud platform certifications (AWS, Azure, GCP). Experience with marketing analytics, modeled propensities, or pre-built segmentation systems. Model and data security best practices, including access control, encryption, and compliance frameworks. Primary Location:
Homebased - Conway, Arkansas We are an equal opportunity employer and do not discriminate in recruiting, hiring, training, promotion or other employment decisions because of race, color, sex, age, religion, national origin, disability, veteran status, or other protected status. Reasonable accommodations are available for applicants with disabilities.
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