AI/ML Product Engineer

About the role

The AI/ML Product Engineer will design, develop, and operationalize artificial intelligence and machine learning solutions that create measurable business value across donor‑facing digital products. The role works closely with product owners, integration engineers, and data teams to embed reliable, compliant AI capabilities into applications and workflows.

Key responsibilities

• Design, train, and evaluate models for predictive analytics, NLP, recommendation systems, and donor behavior modeling, including generative AI and LLM‑based workflows.
• Build and maintain MLOps pipelines, model registries, automated retraining, A/B testing, and monitoring dashboards using tools such as MLflow, Kubeflow, SageMaker Pipelines, or Azure ML Pipelines.
• Deploy models in cloud environments (AWS SageMaker, Azure ML, Google Vertex AI) with CI/CD integration, ensuring observability, guardrails, and cost management.
• Integrate ML inference into MuleSoft, Informatica, and AWS Step Functions pipelines and expose secure APIs for internal and external applications.
• Develop feature stores and data transformation pipelines, partnering with data engineers to ensure high‑quality, governed data for training and inference.
• Ensure compliance with HIPAA, GDPR, and 21 CFR Part 11, applying responsible AI principles, bias mitigation, and audit trails.

Required profile

• 5+ years of experience in machine learning engineering or applied AI, with at least 2 years in a product/platform context.
• Bachelor’s degree in Computer Science, Data Science, Statistics, Mathematics or related field; Master’s or PhD preferred.
• Proven ability to communicate model results, limitations, and trade‑offs to non‑technical stakeholders.

Required skills

• Python and ML libraries: scikit‑learn, TensorFlow, PyTorch, XGBoost, Hugging Face Transformers.
• Cloud ML platforms: AWS SageMaker, Azure Machine Learning, Google Vertex AI.
• MLOps tools: MLflow, Kubeflow, SageMaker Pipelines, Azure ML Pipelines.
• LLM frameworks and evaluation: LangChain, LangGraph, Ragas, LangSmith, DeepEval.
• Observability tools: Langfuse, Helicone, Arize AI, WhyLabs.
• Guardrail frameworks: NeMo Guardrails, Guardrails AI.
• SQL, Spark, Pandas/Polars, Docker, Kubernetes, CI/CD pipelines.