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    Director, MLOps Engineering - New York, United States - Aegistech

    Aegistech
    Aegistech New York, United States

    1 week ago

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    Description

    A Full-time, Director of MLOps Engineering job is available with our client, a leader in the financial industry, Data Analytics, located in NYC. This is a remote role.

    Want to apply Read all the information about this position below, then hit the apply button.

    The Data Science COE at our client in delivers AI capabilities and advancements to our Ratings products and services. AI ML team comprised of experts in AI ML modeling, ML engineers and data science and data engineering teams.

    Responsibilities:

    • MLOps Strategy: Develop and implement MLOps strategies, best practices, and standards to enhance AI ML model deployment and monitoring efficiency.
    • Develop roadmap and strategy for MLOps and LLMOps Platforms and model lifecycle implementation.
    • ML Architecture Design and Development: Responsible for the design and development of custom architecture for batch and stream processing-based AI ML pipelines including data ingestion to preprocessing to scaled AI model compute and ensure the architecture meets all SLA requirements.
    • Work closely with members of technology and business teams in the design, development, and implementation of Enterprise AI platform.
    • Infrastructure Management: Oversee the design, deployment, and management of scalable and reliable infrastructure for model training and deployment.
    • Model Deployment: Lead the deployment of machine learning models into production environments, ensuring reliability and scalability.
    • Monitoring and Optimization: Create and maintain robust monitoring systems to track model performance, data quality, and Infrastructure health. Identify and implement optimizations to improve system efficiency.
    • Automation: Develop and maintain automated pipelines for model training, testing, and deployment, optimizing for speed and reliability. Ensure CI-CD best practices are followed.
    • Internal Collaboration: Collaborate closely with data scientists, machine learning engineers, and software engineers to ensure smooth integration of machine learning models into production systems.
    • Stakeholder Engagement and Collaboration: Collaborate closely with business and PM stakeholders in roadmap planning and implementation efforts and ensure technical milestones align with business requirements.
    • Security and Compliance: Implement security measures and compliance standards to protect sensitive data and ensure adherence to industry regulations.
    • Mentorship: Recruit, develop and mentor technical AI/ML engineering talent on the team Provide guidance and mentorship to junior MLOps engineers, fostering their professional growth and development.
    • Documentation: Maintain comprehensive documentation of MLOps processes and procedures for reference and knowledge sharing.
    • Standards and Best Practices: Ensure the use of standards, governance and best practices in ML pipeline monitoring and ML model monitoring, and adherence to model and data governance standards.
    • Problem Solving: Troubleshoot complex issues related to machine learning model deployments and data pipelines and develop innovative solutions.

    What We're Looking For:

    • Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
    • 7+ years experience as ML engineer, architect, engineer, lead data scientist in Big Data ecosystem or any similar distributed or public Cloud platform, with a desire to assume greater responsibilities as a leader and mentor, while still being hands-on.
    • 4+ years hands-on experience in integrating, evaluating, deploying, operationalizing ML and LLM models at speed and scale, including integration with enterprise applications and APIs. (In addition, ideal candidate should also have hands on experience on training and fine-tuning ML and LLM models at scale).
    • Expertise (4+ years) in distributed computing and orchestration technology (Kubernetes, Ray, Airflow) and scaling, as well as public cloud platform & systems (AWS, GCP, Azure).
    • Proficiency with Databricks, MLflow, Flink, GPT4All, , or similar AI/ML/ML Ops technologies.
    • Experience developing with SQL, NoSQL, ElasticSearch, MongoDB, and Spark, Python, PySpark for model development and ML Ops.
    • Excellent written & verbal communication and stakeholder management skill.
    • Strategic thinker and influencer with demonstrated technical and business acumen and problem-solving skills.
    • Experienced with LLMs (extractive and generative), fine-tuning and operationalizing LLM pipelines. Strong familiarity with higher level trends in LLMs and open-source platforms.

    After you've applied, connect directly to the recruiter at

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