Machine Learning Engineer

Role Overview

As an ML Engineer at Fractal Analytics, you will be at the forefront of building and deploying AI-driven solutions that address critical business challenges across industries. You’ll work with cutting-edge AI platforms to develop both custom and pre-built enterprise applications, tackling key problems such as demand forecasting, asset reliability planning, and inventory optimization.

Your role will focus on implementing scalable, production-ready machine learning solutions that seamlessly integrate into our clients' operations. You’ll collaborate with cross-functional teams, optimize model performance, and ensure the reliability and efficiency of AI applications in real-world business environments. If you're passionate about transforming businesses with AI and thrive in a dynamic, impact-driven environment, we’d love to have you on our team.

Responsibilities Include

• Research, design, implement, and deploy Machine Learning algorithms for enterprise scale applications.
• Engage with clients to translate business needs into technical requirements and Machine Learning solutions.
• Contribute to the design and implementation of features for new and existing enterprise AI solution offerings.
• Provide ongoing support and monitoring for solutions running in production.
• Continuously research and stay abreast of the latest advancements in machine learning and AI to apply cutting-edge techniques in our solutions.

Qualifications

• MS in Computer Science, Electrical Engineering, Data Science, Statistics, Mathematics, or a related field with a strong emphasis on Machine Learning or Artificial Intelligence.
• Excellent programming skills in Python.
• Strong proficiency in Python and ML frameworks like TensorFlow, PyTorch, or Scikit-learn.
• Applied Machine Learning and AI experience in a professional setting.
• Broad knowledge on ecosystem of machine learning and AI algorithms, including tradeoffs to consider and when most appropriate to use.
• Demonstrated project expertise in supervised/unsupervised learning techniques, deep learning, time series, operations research, or Generative AI.
• Familiarity with ML model lifecycle including key considerations and relevant tools (MLFlow, AirFlow, Kubeflow, etc.) for supporting and governing models at scale.
• Familiarity with scalable AI technologies such as (MapReduce, Spark, streaming).
• Prior exposure to cloud computing services like AWS, Azure, or Google Cloud is advantageous (especially ML and AI toolkits).
• Ability to drive a project and work both independently and in a team.
• Smart, motivated, can-do attitude, and seeks to make a difference.
• Excellent verbal and written communication.