Artificial Intelligence (AI) Engineer

Job Title: Artificial Intelligence (AI) Engineer III
Location: Melbourne Florida 32934
Hybrid: 2-3 days per week in office (Working Hours: 8am - 5pm)

Travel: 20%, mostly domestic, customer sites or conference

Must Have

• advanced python
• CI/CD
• CNNs, RNNs, Transformers
• Docker Container
• Docker Swarm
• Kubernetes
• LLM
• REST, gRPC
• vector database

Position Summary:

An AI (Artificial Intelligence) Engineer develops and trains AI models to automate processes and solve complex problems. They design and implement AI systems, ensuring they function effectively and align with business 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:

• Masters degree in Computer Science, Engineering, or a related field. Proven experience as an AI Engineer or in a similar role. Strong programming skills in languages such as Python, R, or Java. Experience with machine learning frameworks and libraries. Excellent analytical and problemsolving abilities. Effective communication and collaboration skills.

Strong Large Language Model (LLM) Expertise

• Handson experience finetuning, adapting, and deploying LLMs, including prompt engineering, embeddings, and context management.

LLM Application & System Architecture

• Proven ability to design and implement productiongrade LLM solutions such as RAG pipelines, agents, and tool/functioncalling systems.

Production MLOps & Model Lifecycle Management

• Experience owning the endtoend ML lifecycle, including CI/CD, deployment, monitoring, versioning, and performance/cost optimization.

Advanced Python & Software Engineering

• Strong Python skills with experience building scalable, testable APIs and services that integrate ML/LLM models into enterprise systems.

CloudBased Scalable ML Infrastructure

• Handson experience with AWS, Azure, or Google Cloud Platform, including containerization (Docker), orchestration (Kubernetes), and GPUbased ML workloads.