AI​/ML Engineer

Work Authorization

Must work directly on W‑2. No sponsorship available. No C2C, CTH, subcontracting, or third‑party vendors permitted.

The AI/ML Engineer will design, build, and deploy machine learning models that power intelligent, data‑driven applications. You’ll work across the ML lifecycle—from data preparation to model training, evaluation, and production deployment. This role requires strong Python skills, familiarity with modern ML frameworks, and the ability to collaborate with engineering and product teams to deliver scalable AI solutions.

• Develop, train, and optimize machine learning models using Python and modern ML frameworks.
• Build and maintain end‑to‑end ML pipelines, including data preprocessing, feature engineering, and model evaluation.
• Deploy models into production environments and integrate them with REST APIs.
• Collaborate with engineering teams to design scalable, reliable ML systems.
• Leverage cloud‑based AI/ML services to accelerate development and deployment.
• Apply statistical and analytical techniques to solve complex business problems.
• Explore and implement advanced AI techniques including NLP, deep learning, and transformer‑based architectures.

• Strong proficiency in Python
  .
• Hands‑on experience with Tensor Flow or Py Torch .
• Understanding of core machine learning algorithms and concepts.

• Experience with data preprocessing tools such as Pandas and Num Py
  .
• Familiarity with cloud AI services (AWS, Azure, or GCP).
• Experience with model deployment
  , MLOps practices, and REST API integration.
• Background in NLP
  , deep learning
  , or neural network architectures.
• Experience with cloud ML platforms such as AWS Sage Maker or Azure AI
  .
• Knowledge of scalable ML system design and distributed training.
• Strong foundation in advanced statistical modeling
  .
• Exposure to transformer models
  , LLMs
  , and cutting‑edge AI frameworks.
• Ability to contribute to or lead AI strategy and roadmap discussions.

You’re passionate about building intelligent systems, experimenting with new ML techniques, and turning data into meaningful insights. You enjoy working across the full ML lifecycle and thrive in environments where innovation and scalability matter.