Description

• Design & Implementation
• Develop and implement scalable AI agent architectures aligned with business requirements, leveraging foundational models.
• Bedrock Model Expertise
• Fine-tune and deploy Amazon Bedrock models (e.g., Large Language Models and multimodal models) for specific use cases.
• Implement solutions involving vector-based retrieval and Retrieval-Augmented Generation (RAG).

Optimization

• Improve model performance while ensuring optimal balance between computational efficiency and cost.

System Reliability

• Ensure deployed AI systems are robust, accurate, scalable, and maintainable over time.

Innovation

• Stay current with advancements in AI/ML technologies and continuously enhance existing solutions.

Collaboration

• Work closely with data scientists, software engineers, product managers, and stakeholders to translate AI research into production-ready systems.

Data & Infrastructure

• Manage large datasets and model lifecycle tools (e.g., MLflow).
• Implement containerization solutions (e.g., Docker) to ensure reproducibility and scalability.

Required Skills & Experience

Technical Skills

• Strong proficiency in Python and ML frameworks such as PyTorch or TensorFlow.
• Hands-on experience with MLOps tools (e.g., MLflow, BentoML, Kubeflow).
• Solid understanding of LLMs, LangChain, and vector databases.
• Experience working with AWS Bedrock services and APIs.
• Familiarity with Docker, CI/CD pipelines, and cloud infrastructure

Key Skills

Education

Bachelor's degree