Principal Machine Learning Engineer 2026 - US

Aimpoint Digital • Atlanta, GA

60d • Remote

About The Position

Aimpoint Digital is a premier analytics consulting firm with a mission to drive business value for clients through expertise in data strategy, data analytics, decision sciences, and data engineering and infrastructure. This position is within our decision sciences practice which focuses on delivering solutions via machine learning and statistical modelling.
What you will do
As a part of Aimpoint Digital, you will focus on enabling clients to get the most out of their data. You will work with all levels of the client organization to build value driving solutions that extract insights and then train them on how to manage and maintain these solutions. Typical solutions will utilize machine learning, artificial intelligence, statistical analysis, automation, optimization, and/or data visualizations. As a Principal Decision Scientist, you will be expected to work independently on client engagements, architect solutions and lead others on engagements, take part in the development of our practice, aid in business development, and contribute innovative ideas and initiatives to our company. As a Principal Decision Scientist, Machine Learning Engineer you will:
Who we are looking for
We are looking for collaborative individuals who want to drive value, work in a fast-paced environment, and solve real business problems. You are a coder who writes efficient and optimized code. You are a problem-solver who can deliver simple, elegant solutions as well as cutting-edge solutions that, regardless of complexity, your clients can understand, implement, and maintain. You genuinely think about the end-to-end machine learning pipeline as you generate robust solutions. You are both a teacher and a student as we enable our clients, upskill our teammates, and learn from one another. You want to drive impact for your clients and do so through thoughtfulness, prioritization, and seeing a solution through from brainstorming to deployment.

Requirements

• Degree in Computer Science, Engineering, Mathematics, or equivalent experience
• Experience designing deploying, and scaling Generative AI and machine learning systems in production, in production, including LLM/Model serving, orchestration, and GPU resource management on Kubernetes for high-volume, business-critical applications
• Experience with managing stakeholders and collaborating with customers
• Strong written and verbal communication skills required
• Ability to manage an individual workstream independently
• 5+ years of experience developing and deploying ML models in any platform (Azure, AWS, GCP, Databricks etc.)
• Ability to apply data science methodologies and principles to real life projects
• Expertise in software engineering concepts and best practices
• Self-starter with excellent communication skills, able to work independently, and lead projects, initiatives, and/or people
• Willingness to travel
• Familiarity with traditional machine learning tools such as Python, SKLearn, XGBoost, SparkML, etc
• Experience with deep learning frameworks like TensorFlow or PyTorch
• Knowledge of ML model deployment options (e.g., Azure Functions, FastAPI, Kubernetes) for real-time and batch processing
• Experience with CI/CD pipelines (e.g., DevOps pipelines, GitHub Actions)
• Knowledge of infrastructure as code (e.g., Terraform, ARM Template, Databricks Asset Bundles)
• Understanding of advanced machine learning techniques, including graph-based processing, computer vision, natural language processing, and simulation modeling
• Experience with generative AI and LLMs, such as LLamaIndex and LangChain
• Understanding of MLOps or LLMOps

Nice To Haves

• Consulting Experience
• Deep expertise in Generative AI and Large Language Models, with a strong focus on optimizing GenAI applications for performance, cost, and reliability
• Familiarity with Agile methodologies, preferably Scrum

Responsibilities

• Define high-level business objectives directly with clients, then develop and execute the project plan to meet those objectives
• Proactively research and apply knowledge within the data science space to deliver best-in-class solutions
• Lead both small and large teams over the entire data science lifecycle – from problem definition to model automation and deployment
• Provide technical leadership to guide development work across teams while also owning and delivering specific technical components yourself
• Manage all aspects of client relationships and create value-driving initiatives for the company
• Design and develop feature engineering pipelines, build ML & AI infrastructure, deploy models, and orchestrate advanced analytical insights
• Write code in SQL, Python, and Spark following software engineering best practices

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Number of Employees: 11-50 employees