Artificial Intelligence (AI) Engineer II (2316)

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Technical Skills Must Have:

• Advanced Python
• CI/CD
• CNNs, RNNs, Transformers
• Docker Container
• Docker Swarm
• Kubernetes
• Large Language Models (LLMs)
• REST, gRPC
• Vector databases
  
  

Location: Melbourne, FL, US Hybrid: 2-3 days per week in office; local candidates preferred

Working Hours: 8:00 AM 5:00 PM

Travel: Approximately 20%, mostly domestic, customer sites or conferences

Interview Process: In-person, 3 rounds Pay Rate: $60-$65/hour

Position Summary: An AI (Artificial Intelligence) Engineer develops and trains AI models to automate processes and solve complex problems. The role involves designing and implementing AI systems, ensuring they function effectively and align with organizational objectives.

Responsibilities:

• Evaluate machine learning processes and select appropriate models
• Collect and analyze large datasets to train AI models
• Develop and deploy AI algorithms and systems
• Collaborate with cross-functional teams to establish goals for AI processes
• Test and validate AI models to ensure accuracy and effectiveness
• Manage data and project infrastructure
• Stay updated on the latest AI developments and technologies
  
  

Qualifications:

• Master s degree in Computer Science, Engineering, or a related field
• Proven experience as an AI Engineer or in a similar role
• Strong programming skills in Python, R, or Java
• Experience with machine learning frameworks and libraries
• Excellent analytical and problem-solving abilities
• Effective communication and collaboration skills
  
  

Key Expertise:

• Large Language Models (LLMs): Hands-on experience fine-tuning, adapting, and deploying LLMs, including prompt engineering, embeddings, and context management
• LLM Application & System Architecture: Ability to design and implement production-grade LLM solutions, such as RAG pipelines, agents, and tool/function-calling systems
• Production MLOps & Model Lifecycle Management: Experience with end-to-end ML lifecycle including CI/CD, deployment, monitoring, versioning, and performance/cost optimization
• Advanced Python & Software Engineering: Building scalable, testable APIs and services that integrate ML/LLM models into enterprise systems
• Cloud-Based Scalable ML Infrastructure: Experience with AWS, Azure, or Google Cloud Platform, including containerization (Docker), orchestration (Kubernetes), and GPU-based ML workloads