AI ML Architect

8-10 years of overall experience in Data Engineering, Ingestion, AI ML / Gen AI related projects. - Specifically, projects involving AI / Gen AI / ML with at least 5 years of experience. - Master's degree with preferred concentrations in Computer Science, Data Science, Math, Actuarial Science, Engineering, or related field. - Proficiency in writing Python or Spark (ScalaSpark or PySpark) and popular machine learning libraries such as TensorFlow, Keras, PyTorch, and conversant with JAVA (Spring AI). - Familiarity with Spring boot services, data preparation, data engineering tasks like data cleaning, feature engineering, and data transformation. - Knowledge of deep learning architectures and techniques like CNNs, RNNs, and reinforcement learning. - Familiarity with AWS data and data science tools including SageMaker, Glue, Lambdas, etc. - Experience in Agile and DevOps development process. - Ability to clearly communicate complex technical concepts to a non-technical audience. (Note: Additional details about the company were not provided in the job description.) Role Overview: As an AI / ML Architect at our company, you will work as an Individual Contributor, focusing on designing, building, and deploying scalable AI / ML solutions. Your role will not involve people management; instead, you will actively write production-grade code, build models, architect ML platforms, and collaborate closely with cross-functional engineering, product, and domain teams. Key Responsibilities:

Collaborate closely with cross-functional teams to develop and deploy AI-powered features and tools that optimize employee experiences and drive business outcomes. - Design, develop, and deploy machine learning models and AI applications to solve complex business problems. - Expertise in designing and developing search and chat applications utilizing Agentic AI. - Design and develop AI paved paths for the engineering organization. - Conduct experiments to evaluate application performance, design experiments to evaluate machine learning model performance, and analyze and interpret results to improve model performance. - Stay updated with new tools and technologies, evaluating their potential impact on the business. - Work with stakeholders to develop and tune algorithms to address business needs, effectively communicate ideas in non-technical terms to educate business partners. - Evaluate, fine-tune, and deploy LLMs and foundation models, implement RAG, guardrails, and prompt engineering best practices. - Ensure LLM security practices are maintained, including secure model deployment, input validation, and data verification. - Write production quality Python code for feature engineering, model evaluation, and interface services (APIs). - Build high-performing ingestion pipelines and expertise across cloud (AWS), data engineering, ML/GenAI, and data ingestion techniques. - Work with CI/CD pipelines (Github Actions, Gitlab,