Models as a Service (MaaS) Technical Lead at Epsilon Inc

Falls Church, VA 22041, USA -

Full Time

Start Date

Immediate

Expiry Date

11 Jun, 25

Salary

0.0

Posted On

11 Mar, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Low Latency, Aws, Disabilities, Scalability, High Availability, Decision Making, High Pressure Situations, Data Science, Computer Vision, Training, Project Direction, Nlp, Sql, Python, Natural Language Processing, Integration, Dexterity, Anomaly Detection, Gitlab, Models

Industry

Information Technology/IT

Description

Responsibilities

Direct the end-to-end development and support of AI models for multiple functional domains, ensuring advanced scalability, reusability, and alignment with organizational goals.
Oversee the integration, maintenance, and sharing of model-specific metadata—such as training datasets, deployment dates, and usage guidelines—promoting robust governance and transparency.
Perform and guide model retraining, performance monitoring, and updates for complex production models (e.g., time series, anomaly detection, natural language processing, LLM-based text summarization), proactively addressing performance gaps.
Collaborate closely with domain experts to capture critical business requirements, and lead efforts to gather, transform, and prepare data for model training and deployment.
Evaluate, implement, and refine state-of-the-art algorithms—including foundational language models, object detection/classification, and time series analysis—to tackle advanced enterprise challenges.
Ensure strict adherence to enterprise standards for metadata management, governance, and security, integrating policy-as-code and compliance checks into the model lifecycle.
Architect and refine APIs for model inference, enabling seamless deployment and integration with large-scale enterprise applications.
Provide technical leadership in model lifecycle management, including versioning, monitoring, and auditing, ensuring all models adhere to best practices and regulatory requirements.
Enhance and maintain automation pipelines for model training, testing, and deployment, leveraging CI/CD principles to improve efficiency and reliability.
Conduct thorough performance benchmarking and optimization for deployed models in production, incorporating feedback loops and monitoring metrics to drive continuous improvement.
Develop and deliver comprehensive training and documentation to enable domain teams to effectively utilize and govern AI/ML models, fostering a self-service culture.
Ensure compliance with organizational policies and regulatory standards throughout the AI/ML model deployment and usage lifecycle, mitigating risks and addressing vulnerabilities.
Research and integrate emerging technologies—such as serverless architectures or specialized hardware accelerators—to expand AI/ML capabilities and scalability.
Monitor and analyze model performance and usage metrics, providing strategic guidance for iterative refinements and alignment with business objectives.
Collaborate with cross-functional teams—including data engineers, platform specialists, and product owners—to incorporate AI models seamlessly into broader enterprise workflows and solutions.
Provide mentorship and direction to junior AI/ML engineers and data scientists, cultivating technical excellence and professional growth within the team