AI / Machine Learning Engineer

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    "description_html": "<p>Title: AI / ML Engineer — Remote (US)</p><p>Location: Remote — United States<br>Employment type: Contract (6–12+ months)<br>Indicative rate: $85–$115/hr</p><p>Role Summary<br>We are seeking an AI / machine learning engineer to develop and deploy ML and GenAI capabilities into production applications. Work spans experimentation, model integration, evaluation, and MLOps on a remote US team.</p><p>Responsibilities</p><ul><li>Build and improve ML models and LLM-powered features (RAG, agents, or classification/NLP as needed)</li><li>Prepare training and evaluation datasets; implement reproducible experiments</li><li>Integrate models into applications via APIs, batch jobs, or real-time services</li><li>Implement monitoring for drift, latency, cost, and quality; iterate from feedback</li><li>Collaborate with product, data engineering, and security on responsible AI practices</li><li>Document architectures, prompts, evaluation metrics, and runbooks</li><li>Support CI/CD and containerized deployment of ML services</li></ul><p>Required Qualifications</p><ul><li>4+ years in machine learning engineering or applied ML roles</li><li>Strong Python and ML libraries (scikit-learn, PyTorch or TensorFlow)</li><li>Experience deploying models to cloud (AWS, Azure, or GCP)</li><li>Familiarity with LLMs, embeddings, and vector search for GenAI use cases</li><li>Solid software engineering practices: Git, testing, code review</li><li>Remote in the United States</li></ul><p>Preferred Qualifications</p><ul><li>LangChain, LlamaIndex, or similar orchestration frameworks</li><li>MLOps: MLflow, SageMaker, Azure ML, Vertex AI, or Kubeflow</li><li>RAG design: chunking, retrieval, evaluation (RAGAS or custom benchmarks)</li><li>Experience with guardrails, PII redaction, and enterprise security reviews</li></ul>",
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