AI Engineer
Houghton Mifflin Harcourt, Indiana, PA, United States
HMH is a learning technology company committed to delivering connected solutions that engage learners, empower educators and improve student outcomes. As a leading provider of K–12 core curriculum, supplemental and intervention solutions, and professional learning services, HMH partners with educators and school districts to uncover solutions that unlock students’ potential and extend teachers’ capabilities.
Position Summary
We are seeking a mid-level AI Engineer to join our team focused on building scalable, production-ready AI solutions that support analytics, automation, and intelligent decision-making. This role will contribute to the development and deployment of machine learning models, generative AI systems, and AI-enhanced data workflows across platforms.
Key Responsibilities
Model Development & Deployment : Design, train, and deploy machine learning and deep learning models using frameworks like TensorFlow, PyTorch, and Scikit-learn.
Generative AI & LLMs : Implement and fine-tune large language models (LLMs) using tools such as LangChain, RAG, and vector databases. Experience with GPTs, LLaMA, or similar models is preferred
MLOps & GenAIOps : Use tools like MLflow and Docker to manage model lifecycle, reproducibility, and scalability. Support production-grade GenAI systems
Data Engineering Collaboration : Work closely with data engineers to ensure robust data pipelines and infrastructure for model training and inference.
Integration & APIs : Develop APIs and microservices to integrate AI models into enterprise applications and workflows.
Monitoring & Optimization : Continuously monitor model performance and retrain as needed to maintain accuracy and relevance.
Security & Governance : Ensure AI systems comply with enterprise security and data governance standards.
Skills & Qualifications
3–5 years of experience in AI/ML engineering or related roles.
Proficiency in Python and experience with AI frameworks (TensorFlow, PyTorch).
Familiarity with cloud platforms (AWS, Azure, GCP) for model deployment.
Experience with MLOps tools (MLflow, Docker) and GenAI deployment.
Strong understanding of LLMs, NLP, and computer vision techniques.
Ability to write clean, efficient, and reusable code.
Experience with RESTful APIs and microservices architecture.
Preferred Experience
Exposure to educational technology or enterprise data environments.
Experience integrating AI into transactional systems.
Familiarity with data warehouses and data governance frameworks.
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Position Summary
We are seeking a mid-level AI Engineer to join our team focused on building scalable, production-ready AI solutions that support analytics, automation, and intelligent decision-making. This role will contribute to the development and deployment of machine learning models, generative AI systems, and AI-enhanced data workflows across platforms.
Key Responsibilities
Model Development & Deployment : Design, train, and deploy machine learning and deep learning models using frameworks like TensorFlow, PyTorch, and Scikit-learn.
Generative AI & LLMs : Implement and fine-tune large language models (LLMs) using tools such as LangChain, RAG, and vector databases. Experience with GPTs, LLaMA, or similar models is preferred
MLOps & GenAIOps : Use tools like MLflow and Docker to manage model lifecycle, reproducibility, and scalability. Support production-grade GenAI systems
Data Engineering Collaboration : Work closely with data engineers to ensure robust data pipelines and infrastructure for model training and inference.
Integration & APIs : Develop APIs and microservices to integrate AI models into enterprise applications and workflows.
Monitoring & Optimization : Continuously monitor model performance and retrain as needed to maintain accuracy and relevance.
Security & Governance : Ensure AI systems comply with enterprise security and data governance standards.
Skills & Qualifications
3–5 years of experience in AI/ML engineering or related roles.
Proficiency in Python and experience with AI frameworks (TensorFlow, PyTorch).
Familiarity with cloud platforms (AWS, Azure, GCP) for model deployment.
Experience with MLOps tools (MLflow, Docker) and GenAI deployment.
Strong understanding of LLMs, NLP, and computer vision techniques.
Ability to write clean, efficient, and reusable code.
Experience with RESTful APIs and microservices architecture.
Preferred Experience
Exposure to educational technology or enterprise data environments.
Experience integrating AI into transactional systems.
Familiarity with data warehouses and data governance frameworks.
#J-18808-Ljbffr