APN Consulting has an immediate need for a direct client requirement: Sr AI/Client Architect
Location - Austin TX (DAY 1 onsite)
Duration Contract

Job Description -

• Proficiency in common Client programming languages, such as Python, and experience with relevant libraries & frameworks like Scikit-learn, TensorFlow, and PyTorch.
• Strong working knowledge of cloud-based Machine Learning platforms like AWS SageMaker, Azure Client, etc.
• Strong understanding of machine learning algorithms, including supervised and unsupervised learning, deep learning, and reinforcement learning, with the ability to build, deploy, and manage models.
• Expertise in the design and implementation of machine learning models, including evaluating performance and fine-tuning parameters for optimal results.
• Knowledge of statistical concepts and methodologies to ensure reliable and accurate model development and validation.
• Hands-on experience with technologies available in the marketplace related to Conversation Bots and adoption.
• Ability to analyze large datasets and apply data preprocessing techniques, feature engineering, and dimensionality reduction.
• Familiarity with cloud computing platforms and distributed computing frameworks for scalable and efficient model training and deployment (e.g., AWS, Google Cloud Platform, Azure).
• Familiarity with GenAI environments like Azure OpenAI, AWS Bedrock, etc., and Large Language Models (LLM).
• Experience on langgraph, crewai & langchain is an advanatage.
• Experience on implementing graph algorithms for the business problem statement is preferred
• Strong problem-solving and critical thinking skills, with the ability to identify business challenges and apply appropriate machine learning techniques to address them.
• Experience in working with structured and unstructured data, including text and time series data, and the ability to develop custom models for specific data types. Excellent communication skills, with the ability to convey complex technical concepts to both technical and non-technical stakeholders.
• Continuous learning mindset and staying up to date with the latest advancements in machine learning and engaging with the research community.

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