AI Integration Specialist

ASM Research

About The Position

The AI Integration Specialist is a pivotal role bridging the gap between cutting-edge AI/ML research and robust, scalable production systems. This individual possesses a deep understanding of data science principles, machine learning model development, and the engineering expertise required to seamlessly integrate AI solutions into existing enterprise applications and workflows. They will be responsible for ensuring that AI initiatives not only deliver accurate and insightful models but are also designed for operational efficiency, maintainability, and measurable business impact, particularly within the Department of Energy's critical missions.

Requirements

Bachelor's or Master’s degree in computer science, Data Science, Artificial Intelligence, Electrical Engineering, or a related quantitative field.
12+ years of experience in a technical field with 5 years of experience in software engineering, data engineering, or MLOps, with a strong focus on deploying and integrating AI/ML models.
Experience working with large datasets, distributed computing, and cloud platforms (e.g., Azure, AWS, GCP).
Strong proficiency in Python is essential; experience with Java, Scala, or Go is a plus.
Experience with MLOps platforms and tools.
Solid understanding of databases (SQL, NoSQL), data warehousing concepts, and data streaming technologies.
Experience with cloud-native services for compute, storage, and AI/ML (e.g., Azure Machine Learning, AWS SageMaker, Google Cloud AI Platform).
Experience designing and implementing RESTful APIs for AI services.
Strong understanding of software development best practices, including version control (Git), testing, and code review.
Excellent problem-solving and analytical skills.
Strong communication and interpersonal skills, with the ability to bridge technical and business gaps.

Nice To Haves

Experience working within a government agency or highly regulated industry.
Familiarity with specific DOE-relevant domains (e.g., energy systems, national security, scientific computing).
Experience with real-time AI inference and low-latency systems.
Certifications in cloud computing (e.g., Azure AI Engineer, AWS Machine Learning Specialty).

Responsibilities

Design, develop, and implement strategies for integrating trained AI/ML models (e.g., predictive analytics, natural language processing, computer vision) into various existing IT systems, operational platforms, and software applications within the DOE.
Work closely with data scientists to understand model requirements, performance characteristics, and potential integration challenges.
Collaborate with software engineers and DevOps teams to establish robust CI/CD pipelines for AI/ML models, ensuring automated testing, deployment, and monitoring.
Develop APIs and microservices to expose AI model functionality for consumption by other applications and services.
Implement and manage MLOps (Machine Learning Operations) best practices, including model versioning, lineage tracking, performance monitoring, drift detection, and retraining strategies.
Establish monitoring dashboards and alerting systems to proactively identify and address issues related to model performance, data quality, and system health.
Act as a key liaison between data science teams and engineering/IT teams, translating complex data science concepts and model requirements into actionable engineering tasks.
Provide technical guidance to data scientists on model design for production readiness, including considerations for efficiency, latency, and resource utilization.
Participate in data exploration, feature engineering, and model experimentation processes to ensure data quality and model interpretability from an integration perspective.
Contribute to the architectural design of AI-enabled systems, advocating for scalable, secure, and resilient solutions.
Research and evaluate new technologies, frameworks, and tools for AI integration, deployment, and MLOps.
Develop and enforce coding standards, documentation practices, and best practices for AI/ML system development.
Communicate technical complexities and integration progress effectively to both technical and non-technical stakeholders, including senior leadership.
Develop training materials and conduct demos for internal teams on AI integration tools, processes, and best practices.