Zinnia
WHO WE ARE: Zinnia is the leading technology platform for accelerating life and annuities growth. With innovative enterprise solutions and data insights, Zinnia simplifies the experience of buying, selling, and administering insurance products. All of which enables more people to protect their financial futures. Our success is driven by a commitment to three core values: be bold, team up, deliver value – and that we do. Zinnia has over $180 billion in assets under administration, serves 100+ carrier clients, 2500 distributors and partners, and over 2 million policyholders. WHO YOU ARE You are a passionate Python and AI/ML Engineer minimum 5 years of hands-on experience building intelligent systems. You thrive in fast-paced environments, love solving complex problems with data and algorithms, and take pride in delivering AI solutions that create real business impact. You have experience with cutting-edge Generative AI, scalable ML pipelines, and production-grade systems and you're energized by working at the frontier of what AI can do.
Design, develop, and deploy machine learning models and Generative AI solutions — including classification, clustering, summarization, search & ranking, and information extraction. Own end-to-end ML pipelines — from data ingestion and preprocessing through model training, deployment, and production monitoring. Collaborate with cross-functional teams to translate business requirements into AI-driven features — applying NLP, outlier detection, and deep learning techniques where applicable. Build robust, scalable, and well-documented Python-based RESTful APIs to expose ML models and AI services in production environments. Optimize database interactions and ensure efficient data storage and retrieval for AI applications across SQL and NoSQL systems. Stay current with the latest advances in AI/ML — integrating emerging approaches such as RAG pipelines, LLM fine-tuning, and vector search into live products. WHAT YOU'LL NEED Python Strong hands-on proficiency for building, scripting, and deploying AI/ML systems. NumPy · Pandas · FastAPI · Scikit-learn Machine Learning Applied expertise across supervised, unsupervised, and deep learning — classification, clustering, outlier detection. PyTorch · TensorFlow · XGBoost · DBSCAN Generative AI (2+ yrs) Hands-on experience building with LLMs — prompt engineering, RAG pipelines, summarization, and AI-powered features. LLMs · RAG · Prompt Eng. · Fine-tuning NLP & Search / Ranking Processes language and builds relevance engines — NER, embeddings, semantic search, and ranking models. spaCy · BERT · FAISS · Elasticsearch API Development Designs and ships secure, well-documented RESTful APIs exposing ML models as production-ready services. REST · FastAPI · OAuth2 · Swagger Databases Proficient in SQL and NoSQL stores for structured and unstructured data pipelines supporting AI workloads. PostgreSQL · MongoDB · Vector DBs GOOD TO HAVE Cloud Platforms Deploys and scales AI workloads on AWS, Azure, or GCP. AWS · Azure TypeScript / JavaScript Frontend or full-stack exposure for building ML-powered product interfaces. TypeScript · React · Node.js MLOps Manages the ML lifecycle — tracking, versioning, and pipeline automation. MLflow · Kubeflow · CI/CD Containerization & Orchestration Packages and scales AI services using containers and cluster management. Docker · Kubernetes