AI Engineer

Job Description

Job Description

Company Description

Redica Systems is a data and analytics SaaS platform that helps regulated industries improve their quality and stay on top of evolving regulations. We are shaping the future of predictive quality with our pioneering work in AI workflows and the development of an exclusive intelligence layer for quality and regulatory data. Our purpose-built processes transform one of the industry’s most complete data sets—aggregated from hundreds of health agencies and unique Freedom of Information Act (FOIA) sourcing—into meaningful answers and insights that reduce regulatory and compliance risk.

Founded in 2010, Redica Systems serves over 200 customers in the Pharma, BioPharma, MedTech, and Food and Cosmetics industries, including 19 of the top 20 Pharma companies and 9 of the 10 top MedTech companies. Redica Systems’ headquarters are in Pleasanton, CA. More information is available at redica.com.

Job Description

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.

Core 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.

What Success Looks Like in the First 6 Months

• Ship AI-powered features used by customers within the Redica platform.
• Contribute production-ready code to backend services and frontend interfaces.
• Help integrate LLM-based capabilities into customer-facing workflows.
• Improve reliability, testing, and performance of AI-enabled services.
• Identify opportunities to automate engineering workflows using AI tools.

About you

• Tech Savvy: Demonstrates strong technical proficiency in AI technologies and modern development tools, and actively adopts emerging technologies that improve system performance and engineering productivity.
• Manages Complexity: Works effectively within complex systems involving AI models, data pipelines, and distributed services.
• Plans and Aligns: Executes development tasks within defined scopes and aligns work with product and engineering priorities.
• Collaborates: Works effectively with cross-functional teams and contributes constructively toward shared goals.
• Manages Ambiguity: Adapts to evolving datasets, model approaches, and product requirements while maintaining steady development progress.
• Engaged: Shares our values and possesses the essential competencies needed to thrive at Redica, as outlined here: -us/careers.

Qualifications

• 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

Bonus Points

• 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

Additional Information

Top pharmaceutical companies, food manufacturers, medtech companies, and service firms from around the globe rely on Redica Systems to mine and process government inspection, enforcement, and registration data. This enables them to quantify risk signals from their suppliers, identify market opportunities, benchmark against peers, and prepare for the latest inspection trends. 

Our data and analytics have been cited by major media outlets including MSNBC, The Wall Street Journal (WSJ), and The Boston Globe.