Job
Description
Key Responsibilities: 1. *Agentic AI Systems*: Design and implement intelligent Agentic AI systems using LLMs, vector databases, and orchestration frameworks. 2. *ML Workflows*: Build, deploy, and maintain ML workflows using AWS SageMaker, Lambda, and EC2 with Docker. 3. *ETL Pipelines*: Develop and manage ETL pipelines using AWS Glue and integrate with structured/unstructured data sources. 4. *Full-Stack Development*: Implement APIs and full-stack components to support ML agents, including visualization tools using Streamlit. 5. *Legacy System Integration*: Reverse-engineer existing codebases and APIs to integrate AI features into legacy or proprietary systems. Required Qualifications: 1. *AWS Experience*: Hands-on experience with AWS services like Lambda, EC2, Glue, and SageMaker. 2. *Python and Full-Stack Development*: Strong Python and full-stack development experience. 3. *LLMs and Vector Search*: Solid grasp of LLMs and vector search engines. 4. *Reverse-Engineering*: Demonstrated ability to reverse-engineer systems and build integrations. 5. *Cloud Infrastructure*: Experience with cloud infrastructure, RESTful APIs, CI/CD pipelines, and containerization. Preferred Qualifications: 1. *RAG and Multi-Agent Systems*: Background in Retrieval-Augmented Generation (RAG), multi-agent systems, or knowledge graphs. 2. *Open-Source LLM Frameworks*: Experience with open-source LLM frameworks like Hugging Face. 3. *Autonomous Task Planning*: Knowledge of autonomous task planning, symbolic reasoning, or reinforcement learning. 4. *Secure AI Systems*: Exposure to secure, regulated, or enterprise-scale AI systems. This role requires a strong blend of technical skills, including AWS, Python, and full-stack development, as well as experience with LLMs, vector search, and agentic AI systems. Show more Show less
