Remote Machine Learning Software Engineer (Power Systems)

Summary

Join ThinkLabs AI as a Staff /Senior Staff Machine Learning Engineer to accelerate the design and delivery of machine learning and generative AI models, technology, and cloud engineering for the operationalization of AI/ML-driven solutions at an enterprise scale.

Requirements

• Master's degree or PhD in Electrical, computer science, power systems, machine learning, natural language processing, or a related field
• Strong knowledge in mathematical modeling, RNNs, CNNs, Transformers, LSTMs, transfer learning, reinforcement learning, imitation learning, GANs, and time-series analysis and modeling of dynamic systems
• Prior experience in operationalizing machine learning workflows
• Hands-on experience with multi cloud technologies
• Prior experience in the electricity & energy domain is preferred
• Strong experience in developing and deploying large-scale ML Models and generative AI systems using frameworks such as numpy, scipy, pandas, scikit-learn, TensorFlow, PyTorch, Hugging Face, or OpenAI)
• Proficient in Python and other programming languages for data analysis and machine learning
• Excellent problem-solving, analytical, and communication skills
• Passionate about natural language processing, generative AI, and creating impactful solutions

Responsibilities

• Design and lead the implementation of robust and scalable data science and machine learning architecture integrated into the product platform
• Working with the RD and Data Science teams, deploy prototype models to production
• Re-train and re-deploy models based on quality parameters collected in continuous monitoring
• Define and monitor quality parameters for ML models in production
• Work closely with data science teams to take newly developed models into production
• Optimize the performance, scalability, and reliability of ML Models and generative AI systems
• Design, implement, and evaluate large-scale ML models and generative AI systems in the energy domain and applications
• Design and implement ML toolchains and data platforms to scale ML solutions in production
• Collaborate with other machine learning engineers, data scientists, and domain experts to understand the requirements and challenges of natural language processing and generation tasks
• Define best practices for data engineering, feature engineering, and model deployment