AI/ML Engineer

Introduction

This position is for an AI/ML Engineer who will be responsible for designing, developing, and deploying AI and machine learning solutions within a hybrid cloud environment. The role involves collaborating with team members to build software systems that integrate AI/ML techniques to automate tasks, assist internal users, and improve user-facing services.

Responsibilities

• System Design & Collaboration:
  • Work within established constraints regarding infrastructure, programming languages, and model selection
  • Contribute to technical decision-making related to data processing, retrieval strategies, and system integration
  • Collaborate with team members to define agent architectures, workflows, and system design decisions
  • Evaluate and select appropriate approaches for given tasks, including determining when to use LLM-based versus non-LLM techniques
• Testing, Evaluation, and Quality Assurance:
  • Assist in the design and implementation of testing and evaluation pipelines for AI/ML systems
  • Develop unit and integration tests for AI-enabled workflows and data pipelines
  • Generate and utilize synthetic data to support evaluation and benchmarking efforts
  • Contribute to improving system performance, including accuracy, latency, and cost efficiency
• Deployment & Operations:
  • Support deployment of AI/ML applications within a hybrid cloud environment
  • Work with containerized applications to ensure reliable deployment and updates
  • Optimize systems for environments with limited computational resources, including minimal GPU availability
• General Responsibilities:
  • Deliver production-grade systems aligned with defined requirements, while supporting iterative improvement of evolving tools
  • Document system designs, workflows, and technical decisions as required
  • Stay informed on relevant advancements in AI/ML and apply them where appropriate within project constraints

Requirements

Skills and Experience required:

• Experience with:
  • SQL and relational database systems (e.g., PostgreSQL)
  • Fine-tuning small language models or embedding models
  • Contributing to or maintaining open-source software projects
  • Graph databases or graph extensions (e.g., Neo4j, Apache AGE)
  • Designing and implementing multi-agent or task-oriented AI systems