AI Engineer
Job Description
AI Engineer
Full TimeMiami, FL
Software Engineering
Essential Responsibilities:
AI Platform Engineering: Build and operate Azure-based AI platform components for independent AI engineering delivery, using Azure ML, Databricks, MLflow, and secure service patterns to make model deployment repeatable and supportable.
MLOps / LLMOps: Develop CI/CD, model registry, evaluation, prompt/version control, retraining, and release workflows that help teams manage ML models and GenAI systems consistently from experimentation through production.
Containerized Deployment: Package and deploy AI services with Docker, Kubernetes, identity controls, environment configuration, and runtime standards that improve reliability, portability, and operational readiness.
GenAI Enablement: Implement GenAI capabilities using GPT-class models, RAG patterns, embeddings, vector databases, prompt engineering, and agent workflows where they improve reuse, quality, and enterprise scalability.
Developer Productivity: Improve engineering productivity through reusable templates, code standards, GitHub/Azure DevOps automation, documentation, and tooling that help Data Science and AI teams ship with fewer manual steps.
Observability: Create monitoring dashboards and alerts for latency, cost, drift, model quality, hallucination risk, token usage, and service health so production AI systems can be managed with clear operating signals.
Architecture Guardrails: Define and apply practical guardrails for security, identity, networking, data access, model serving, and GenAI evaluation so teams can move faster without bypassing enterprise controls.
Point Projects: Own targeted technical projects that expand independent AI engineering delivery, such as reusable deployment patterns, feature pipelines, evaluation harnesses, vector search services, or cost-optimization improvements.
Cross-Team Collaboration: Work closely with Data Science, IT, Security, and business teams to make platform capabilities usable in real delivery contexts while maintaining clear ownership for engineering foundations and production operations.
Qualifications / Knowledge / Skills:
Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field, or equivalent practical experience building production-grade AI systems.
Experience: Demonstrated experience appropriate to mid scope with production AI/ML or GenAI systems, including the ability to move beyond prototypes into reliable, monitored, and maintainable services.
Cloud Infrastructure: Hands-on experience with Azure infrastructure patterns, including Azure ML, Databricks, Azure DevOps, identity, networking, secrets, compute, and deployment environments.
MLOps: Experience with MLflow, Azure ML pipelines, model registries, feature pipelines, deployment approvals, monitoring, retraining, and model lifecycle management.
LLMOps: Experience managing GenAI systems through prompt/version control, RAG evaluation, embeddings, vector database operations, token/cost monitoring, safety testing, and quality measurement.
Distributed Systems: Understanding of scalable service design, asynchronous processing, failure modes, observability, and performance tuning for production AI workloads.
Security: Knowledge of enterprise authentication, authorization, data protection, secure model serving, network boundaries, and compliance-aware deployment practices.
Engineering: Strong Python engineering skills with modern development practices, including testing, code review, Git workflows, packaging, APIs, Docker, and CI/CD automation.
Communication: Clear communication skills with data scientists, IT partners, and business product teams, including the ability to explain architecture decisions, delivery risks, tradeoffs, and production-readiness expectations without unnecessary jargon.