The AI/ML Engineer will design, build, and deploy production-grade machine learning and AI systems that power core products and features. This role bridges cutting-edge research with reliable, scalable engineering, turning prototypes into high-performance services that run 24/7 in production. Key Responsibilities:<ul><li>Design and implement end-to-end ML pipelines: data ingestion, feature engineering, training, evaluation, deployment, and monitoring</li><li>Develop, optimize, and productionize models using PyTorch/TensorFlow/JAX (including LLMs, vision, multimodal, and custom architectures)</li><li>Optimize inference for latency, memory, and cost (quantization, pruning, distillation, TensorRT, ONNX, vLLM)</li><li>Integrate models into backend systems via REST/gRPC APIs, event-driven architectures, or real-time serving</li><li>Own MLOps practices: experiment tracking (MLflow, W& B), model registry, CI/CD for ML, canary deployments, drift detection, and observability</li><li>Collaborate with data scientists to harden research prototypes into clean, tested, production-ready code</li><li>Build and maintain retrieval-augmented generation (RAG), agentic workflows, and prompt-engineered systems when appropriate (LangChain, LlamaIndex)</li><li>Continuously monitor, retrain, and improve live models to maintain performance and reliability</li></ul>

<ul><li>Bachelor’s or Master’s in Computer Science, Engineering, Mathematics, or equivalent experience</li><li>3–8+ years of hands-on experience shipping ML/AI systems to production</li><li>Expert-level Python and deep proficiency in PyTorch (preferred) or TensorFlow/JAX</li><li>Proven track record with modern ML infrastructure: Docker, Kubernetes, Ray, Triton Inference Server, cloud ML platforms (SageMaker, Vertex AI, Bedrock)</li><li>Strong MLOps experience (MLflow, Airflow, feature stores, model registries, monitoring tools)</li><li>Solid software engineering fundamentals: testing, code reviews, system design, versioning</li><li>Experience integrating models into larger systems (FastAPI, microservices, streaming pipelines)</li></ul> Preferred Qualifications:<ul><li>Hands-on experience fine-tuning and deploying open-source LLMs (Llama 3, Mistral, Gemma, etc.)</li><li>Built RAG systems, AI agents, or copilot-style applications using LangChain, LlamaIndex, or DSPy</li><li>Familiarity with prompt engineering, chain-of-thought, ReAct, and tool-use patterns</li><li>Contributions to open-source ML projects or published research</li><li>Experience with GitHub Copilot / Cursor for accelerated development</li><li>Knowledge of distributed training (DeepSpeed, FSDP, Megatron) and large-scale data processing (Spark, DuckDB, Polars)</li></ul>

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West Palm Beach, FL
onsite
Temporary
- USD / Hourly
The AI/ML Engineer will design, build, and deploy production-grade machine learning and AI systems that power core products and features. This role bridges cutting-edge research with reliable, scalable engineering, turning prototypes into high-performance services that run 24/7 in production. Key Responsibilities:<ul><li>Design and implement end-to-end ML pipelines: data ingestion, feature engineering, training, evaluation, deployment, and monitoring</li><li>Develop, optimize, and productionize models using PyTorch/TensorFlow/JAX (including LLMs, vision, multimodal, and custom architectures)</li><li>Optimize inference for latency, memory, and cost (quantization, pruning, distillation, TensorRT, ONNX, vLLM)</li><li>Integrate models into backend systems via REST/gRPC APIs, event-driven architectures, or real-time serving</li><li>Own MLOps practices: experiment tracking (MLflow, W& B), model registry, CI/CD for ML, canary deployments, drift detection, and observability</li><li>Collaborate with data scientists to harden research prototypes into clean, tested, production-ready code</li><li>Build and maintain retrieval-augmented generation (RAG), agentic workflows, and prompt-engineered systems when appropriate (LangChain, LlamaIndex)</li><li>Continuously monitor, retrain, and improve live models to maintain performance and reliability</li></ul>
2025-12-02T15:27:20Z