Zinnia - Software Engineer I
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Requirements
• You are a passionate Python and AI/ML Engineer minimum 4 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. • PythonStrong hands-on proficiency for building, scripting, and deploying AI/ML systems.NumPy · Pandas · FastAPI · Scikit-learnMachine LearningApplied expertise across supervised, unsupervised, and deep learning — classification, clustering, outlier detection.PyTorch · TensorFlow · XGBoost · DBSCAN • Python • 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 • Generative AI (2+ yrs) • NLP & Search / RankingProcesses language and builds relevance engines — NER, embeddings, semantic search, and ranking models.spaCy · BERT · FAISS · Elasticsearch • NLP & Search / Ranking • API DevelopmentDesigns and ships secure, well-documented RESTful APIs exposing ML models as production-ready services.REST · FastAPI · OAuth2 · Swagger • API Development • DatabasesProficient in SQL and NoSQL stores for structured and unstructured data pipelines supporting AI workloads.PostgreSQL · MongoDB · Vector DBs • Databases • GOOD TO HAVE • Cloud PlatformsDeploys and scales AI workloads on AWS, Azure, or GCP.AWS · Azure • Cloud Platforms • TypeScript / JavaScriptFrontend or full-stack exposure for building ML-powered product interfaces.TypeScript · React · Node.js • TypeScript / JavaScript • MLOpsManages the ML lifecycle — tracking, versioning, and pipeline automation.MLflow · Kubeflow · CI/CD • MLOps • Containerization & OrchestrationPackages and scales AI services using containers and cluster management.Docker · Kubernetes • Containerization & Orchestration
Responsibilities
• 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.
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