AI / ML Engineer
E Source
| Company | E Source |
| Category | Engineering |
| Location | Remote |
| Remote | Remote |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 8 Jun 2026 |
| Last verified | 30 Jul 2026 |
| Source | Employer career page (ashby) |
Description
At E Source, we help utilities make sense of complexity in a rapidly changing landscape, and we’re looking for an AI / ML Engineer to help shape how that impact shows up in the world.
E Source is a research, data/analytics, and technology focused professional services firm focused exclusively on the utility industry in the US and Canada. We help utilities target and serve their customers more effectively, enhance and optimize their grid, and leverage operating best practices and technologies to manage their business more effectively. Headquartered in Texas, we have 450+ employees across the US and Canada. Learn more at www.esource.com http://www.esource.com
Joining E Source as a Senior AI/ML Engineer is an exciting opportunity to work with a dynamic and talented team, leveraging your skills to drive meaningful impact in the utility industry. You’ll contribute to the next generation of AI and ML solutions that blend predictive modeling, optimization, and generative reasoning to empower sustainable decisions.
If you are a self-motivated engineer who thrives in a collaborative, innovative environment, and if you’re passionate about building intelligent, scalable AI/ML systems, we invite you to apply and help shape the future of utilities.
As a Senior AI/ML Engineer at E Source, you’ll play a crucial role in our machine learning engineering team. Collaborating with data scientists and software engineers, you’ll contribute to the development of cutting-edge ML and AI products that support our mission of building a sustainable future with utilities.
Your expertise will be instrumental in building tools and pipelines for developing and scaling machine-learning models, including emerging AI system design components and agentic architectures. You’ll work across a range of use cases—like electrification, network reliability, geospatial analysis, time series forecasting, image and text processing, and AI-driven decision systems—using both traditional ML and modern generative AI approaches.
In this role, you will:
- Collaborate with cross-functional teams to design, develop, and deploy scalable software products that incorporate machine learning and AI models.
- Build reusable Python packages to support the implementation of ML/AI algorithms and data-processing pipelines.
- Contribute to the design of AI systems, including components for retrieval-augmented generation (RAG), LLM integration, and agent-based workflows.
- Develop agentic evaluation and monitoring frameworks to assess model reasoning, consistency, and fairness.
- Evaluate database design and create optimized performance queries for efficient data processing and retrieval.
- Break down complex MLE and AI tasks into manageable user and technical stories, ensuring efficient and effective implementation.
- Ensure high-quality test coverage of ML code and participate in peer reviews to provide valuable recommendations.
- Stay updated on the latest advances in machine learning engineering, generative AI, and AI system orchestration, and incorporate relevant practices into our workflows.
- Contribute to continuous delivery and Agile development processes, adhering to best practices in ML and AI engineering.
You’re likely a great fit if you:
- Master’s degree in computer science, software engineering, data science, or a related field (PhD preferred).
- Minimum of 7 years of professional experience designing, developing, and deploying machine learning software products independently and collaboratively.
- Strong programming skills in Python, with experience developing reusable packages and automation tools.
- Familiarity with Databricks for scalable data processing and collaborative analytics.
- Solid understanding of machine learning systems design concepts, including model lifecycle management, MLOps, and scalable inference.
- Hands-on experience with cloud infrastructure (Azure, AWS, or GCP), containerization
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