AL/ML Engineer -Austin, TX

AL/ML Engineer (2 Positions)

Austin, TX

6 Months Contract

Key Responsibilities:

• Design and Develop AI/ML Models: Build, train, and optimize machine learning models, including Generative AI applications such as language models, image generation, or custom generative pipelines.
• Deploy and Monitor ML Pipelines: Implement end-to-end machine learning pipelines on cloud platforms (Google Cloud Platform or AWS), ensuring scalability, performance, and reliability.
• Collaborate on AI Solutions: Work closely with cross-functional teams, including product managers and data engineers, to design AI-driven solutions that address business challenges.
• Research and Innovation: Stay up-to-date with the latest trends and advancements in AI/ML and Generative AI, applying relevant innovations to projects.
• Optimize Cloud Resources: Utilize cloud-native services (e.g., AWS SageMaker, Google Cloud Platform AI Platform) to manage and deploy machine learning workflows effectively and cost-efficiently.
• Documentation: Maintain clear and detailed documentation of AI/ML models, pipelines, and processes.

Required:

• Experience: 5 8 years of experience in AI/ML development, with at least 1 2 years of hands-on experience with Generative AI applications.
• Programming: Strong proficiency in Python and ML frameworks like TensorFlow, PyTorch, or similar.
• Cloud Platforms: Working knowledge of Google Cloud Platform (e.g., Vertex AI, BigQuery) or AWS (e.g., SageMaker, Lambda).
• AI/ML Tools: Experience with model training, evaluation, and deployment tools such as MLFlow, Kubeflow, or equivalent.
• Data Handling: Solid understanding of data preprocessing, feature engineering, and model optimization techniques.
• Problem Solving: Strong analytical skills with the ability to solve complex problems using AI/ML solutions.

< style="margin: 0cm;">Preferred:

• Knowledge of Large Language Models (LLMs) and fine-tuning techniques.
• Familiarity with containerization and orchestration tools like Docker and Kubernetes.
• Experience in deploying APIs or microservices for AI solutions.
• Certifications in AI/ML or cloud platforms (e.g., AWS Certified Machine Learning Specialty, Google Professional Machine Learning Engineer).

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.