Machine Learning Engineer

Duration: 17 weeks - high chance of extension based upon performance

• Location: fully remote as long as located near Exelon satellite in Chicago, Philadelphia, Washington D.C., Baltimore, Delaware, etc
• Work hours: FT - 9am - 5pm EST / 8am - 4pm EST
• Education preference: MS in Computer Science/Data Science
• Exp: At least 5 years of industry/corporate experience is preferred
• Previous experience in utility industry is preferred but not required
• Great communication skills, ability to independently identify and drive work to achieve the objectives/milestones.

Required Qualifications for the Machine Learning Engineer:

• 5 years of experience in Python development with a focus on deep learning.
• Hands-on experience with transformers, Hugging Face, and custom model training.
• Proven track record of deploying models on GPU-based infrastructure.
• Strong understanding of API design principles and microservices architecture.
• Experience with Azure cloud services, Docker, and GitHub Actions.
• Excellent problem-solving skills and ability to work in a fast-paced, collaborative environment.

Preferred Qualifications for the Machine Learning Engineer:

• Experience with multi-modal models or generative AI applications.
• Familiarity with MLOps tools and practices.
• Contributions to open-source AI projects or publications in relevant fields.

Job Description
Position Overview for the Machine Learning Engineer:
We are seeking a highly skilled and motivated Machine Learning/Deep Learning Engineer with expertise in neural network development, GenAI technologies, and cloud-native deployment on Azure. This role will be instrumental in designing, training, and deploying advanced AI models across text, image, and audio domains, while also managing scalable cloud infrastructure and APIs.
Key Responsibilities
Deep Learning & Neural Networks

• Design and implement deep learning models using TensorFlow, PyTorch, and transformer architectures.
• Fine-tune pre-trained models for domain-specific tasks involving text, image, or audio datasets.
• Optimize and deploy models on NVIDIA GPU hardware (e.g., A100, H100) for high-performance inference.
• Develop LLMOps context aware pipelines for chat applications using python frameworks.
• Collaborate with data scientists and product teams to integrate models into production systems.

API Development & Integration

• Develop and maintain RESTful and gRPC APIs for model serving and data access.
• Manage the full API lifecycle, including versioning, documentation, and security.
• Integrate APIs with internal and external applications using FastAPI or similar Python frameworks.

Azure Cloud Engineering

• Create and manage Azure Container Apps, Container Registries, and Docker images.
• Deploy and monitor Azure Web Apps for hosting AI services and dashboards.
• Automate CI/CD pipelines using GitHub Actions for seamless deployment and updates.

#tech