AI Engineer

REDICA Systems•Pleasanton, CA

10h

About The Position

We’re looking for an AI Engineer to join our team as we continue to develop the first-of-its-kind Quality and Regulatory Intelligence (QRI) platform for the life sciences industry. In this role, you will help build and deploy AI-powered capabilities that extract insights from complex regulatory datasets, inspection reports, and government data sources. You will work closely with product managers, data engineers, and software engineers to integrate LLM-powered systems into the Redica platform. The ideal candidate maintains a high bar for engineering quality while remaining hands-on in the code, building scalable AI services and applications that operate reliably in production environments.

Requirements

3+ years of experience as an ML Engineer developing and productionizing traditional ML models and/or Generative AI applications
Hands-on experience in Python
Strong experience in building and deploying LLM and Generative AI applications at scale
Extensive hands-on experience with third-party LLM provider APIs (OpenAI, Google, Anthropic, Amazon Bedrock) and open-source LLMs (Llama, Mistral)
Experience in building conversational systems using LLMs and agentic frameworks (Langchain, LlamaIndex, Langgraph, CrewAI)
Hands-on experience with microservices architecture and orchestration, including building backend APIs using FastAPI
Experience with vector databases (e.g., Pinecone), graph databases (e.g., Neo4J), and hybrid search
Hands-on experience working with SQL (e.g., Postgres, Snowflake) and NoSQL (e.g., DynamoDB) databases/warehouses
Bachelor's degree in Computer Science, Computer Engineering, or a related technical field

Nice To Haves

Familiarity with lightweight UI design using Python/JavaScript frameworks (Streamlit, ReactJS) and integration with ML model backends
Hands-on experience with container orchestration services on AWS (e.g., ECS and EKS) and ML deployment on AWS (AWS Sagemaker)
Experience with both batch and event-driven application architectures and ML inference methods

Responsibilities

Build and deploy AI-powered applications using large language models and generative AI frameworks.
Develop conversational systems and intelligent workflows using LLMs and agentic frameworks.
Integrate AI capabilities into existing platform services and APIs.
Design and implement backend APIs and services supporting AI functionality using Python and FastAPI.
Develop microservices that enable scalable AI inference and data processing.
Integrate AI services with other platform components to deliver end-to-end product capabilities.
Work with structured and unstructured regulatory datasets to power AI-driven insights.
Implement hybrid search and retrieval workflows using vector databases and graph databases.
Integrate AI models with data pipelines and data stores to support scalable inference.
Deploy and maintain AI systems in production environments.
Contribute to testing, monitoring, and performance optimization of AI services.
Assist in troubleshooting production issues related to AI systems and model inference.
Work closely with product managers and engineering teams to translate product requirements into AI-powered solutions.
Participate in engineering discussions, code reviews, and sprint planning.
Contribute to continuous improvement of AI development practices and system performance.