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.

• 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).