Sr. Director MLOps Engineering

Work set-up: 
Full Remote
Contract: 
Experience: 
Senior (5-10 years)
Work from: 

Offer summary

Qualifications:

Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or related fields., 15+ years of ML platform engineering experience, including cloud modernization., Proven track record designing hyperscale ML platforms across AWS, Azure, and GCP., Deep understanding of MLOps, model governance, and modern ML architectures..

Key responsibilities:

  • Lead the design and development of scalable MLOps platforms supporting data products.
  • Collaborate with cross-functional teams to define requirements and modernize ML infrastructure.
  • Oversee migration of existing capabilities to cloud-native ML solutions and develop best practices.
  • Mentor junior engineers and ensure compliance with industry standards and security policies.

Acxiom logo
Acxiom Marketing & Advertising Large https://www.acxiom.com/
1001 - 5000 Employees
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Job description

Acxiom is seeking a highly experienced and visionary Senior Director MLOps Engineering to spearhead the design and development of scalable MLOps platform to support Acxiom’s modeled data product builds. This role demands a deep understanding of cuttingedge AIML and MLOps technologies, coupled with a passion for building robust platforms that empower our modeled product development at scale. As a thought leader, you will be instrumental in leading the modernization efforts and shaping the future of our ML platforms, enabling modeled product development, advanced marketing analytics, and driving impactful customer engagement strategies.

You will collaborate closely with Senior architects, Data Scientists, ML Practitioners and DevOps engineers, across Acxiom to evaluate existing MLOps tools & technologies, define futurestate architecture, and implement scalable, cloudnative solutions leveraging platforms such as Databricks, Snowflake, and other enterprise services on AWS, GCP, and Azure.

This role offers the flexibility to be located almost anywhere within the U.S.

What You Will Do:

  • Collaborate with Acxioms 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 codevelopment 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 realtimenear realtime workloads.
  • Lead endtoend 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 proofofvalue (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 AIML and GenAI projects, ensuring adherence to industry standards, regulatory requirements, Responsible AI principles, and AcxiomIPG architectural guidelines.
  • Create and maintain reference ML architectures, patterns, and best practices for the AIML lifecycle and its integration within Acxioms 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, CICD, 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 thirdparty data collaboration.
  • Stay abreast of evolving data and cloud technologies to provide clients with futureready solutions.
  • Mentor junior MLOps engineers across the organization to foster expertise and amplify impact.
    • What You Will Have:

      • Bachelors or Masters 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 largescale ML platforms.
      • Proven track record of designing and delivering hyperscale ML platforms across AWS, Azure, and GCP.
      • 10+ years of experience optimizing Sparkbased ML inference workloads and associated performance tuning.
      • Proven Ability to develop large case ML solutions using H20, SparkML, scikitlearn and other ML tools.
      • Demonstrated expertise in implementing MLOps pipelines and solutions using lowerlevel programming languages such as CC++ or Java for optimal performance.
      • Demonstrated experience in ML workload migration projects (e.g., Teradata, Hadoop, Oracle to DatabricksSnowflake).
      • 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.
      • 810+ 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 CICD 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 crossfunctional 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.

Required profile

Experience

Level of experience: Senior (5-10 years)
Industry :
Marketing & Advertising
Spoken language(s):
English
Check out the description to know which languages are mandatory.

Other Skills

  • Communication
  • Leadership
  • Detail Oriented
  • Problem Solving

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