Title: AI Engineer
Location: Melbourne, FL
Duration: 12 Months contract
Shift: 8am-5pm
Hybrid: 2-3 days per week in office; local candidates
Travel: 20%, mostly domestic, customer sites or conference
Job Description:
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 problem solving abilities. Effective communication and collaboration skills.
- -- Strong Large Language Model (LLM) Expertise
Hands‑on experience fine‑tuning, adapting, and deploying LLMs, including prompt engineering, embeddings, and context management.
- -- LLM Application & System Architecture
Proven 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 owning the end‑to‑end 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.
- -- Cloud‑Based Scalable ML Infrastructure
Hands‑on experience with AWS, Azure, or GCP, including containerization (Docker), orchestration (Kubernetes), and GPU‑based ML workloads.