AI Engineer

Key Responsibilities Design and implement AI/ML models for business use-cases, including LLM/GenAI solutions and traditional ML models. Select suitable algorithms and architectures based on data characteristics, performance needs, and use-case complexity. Perform feature engineering, hyperparameter tuning, and model validation to ensure accuracy and generalization. Build and optimize workflows for training, evaluation, and inference, ensuring performance and reliability. Evaluate models using statistical metrics and real-world testing; ensure robustness, fairness, and responsible AI practices. Collaborate with business stakeholders, product managers, and engineers to integrate models into production systems. Monitor deployed models, track drift, and retrain/refine models to maintain performance over time. Maintain strong documentation for reproducibility, audits, and knowledge sharing. Stay up to date with advancements in transformers/LLMs, GenAI patterns (RAG, fine-tuning), GNNs, and modern MLOps tooling. Required Skills & Qualifications 5+ years of experience in Machine Learning / AI Engineering. Strong proficiency in Python and TypeScript. Strong foundations in ML concepts: supervised/unsupervised learning, validation strategies, and statistical evaluation. Hands-on experience with ML libraries such as: PyTorch / TensorFlow scikit-learn Experience building and deploying models to production (APIs, batch pipelines, integrations, monitoring). Preferred / Nice to Have Experience with Generative AI / LLMs, including patterns like: Prompt engineering, RAG, embeddings, vector search, fine-tuning Familiarity with GenAI/ML tooling such as: Hugging Face, MLflow, Kubeflow Insurance domain exposure: claims, underwriting, fraud detection, risk modeling. Experience with Palantir Foundry and AIP Functions (highly preferred).