Cygnify Hiring! Full Time Backend - Platform Engineer (AI - Machine Learning) in - Ricebowl

Role Summary

We are recruiting on behalf of a premier regional insurtech innovator looking for a versatile Backend & Platform Engineer (AI / Machine Learning). This role bridges the gap between scalable server-side systems and production-grade ML infrastructure. You will be responsible for designing high-throughput backend services, orchestrating low-latency inference pipelines, and establishing the robust MLOps foundation that powers real-time underwriting, claims automation, and personalized insurance products.

Key Responsibilities

• Design, build, and scale resilient backend services and distributed APIs (REST, gRPC) supporting AI-powered mobile and web features.
• Construct and maintain high-availability model-serving infrastructure optimized for high-throughput, low-latency inference and real-time data retrieval.
• Build automated pipelines for data ingestion, feature generation, model deployment, and continuous evaluation (MLOps).
• Collaborate with data science and mobile teams to build robust API contracts, vector search systems, and caching mechanisms.
• Implement production-grade observability, tracking system health, inference latency, error rates, and model drift under real-world traffic.
• Ensure data security, strict regulatory compliance, and system resilience across all cloud-native distributed environments

Our Ideal Candidate

• Core Engineering Background: 3+ years of professional backend software engineering or distributed systems experience using languages such as Python, Go, or Java.
• AI/ML Systems Acumen: Practical exposure to model serving frameworks (e.g., Triton, TorchServe, vLLM, or custom FastAPI engines), vector databases, or feature stores.
• Cloud & Infrastructure: Hands-on experience with containerization and orchestration (Docker, Kubernetes) and managed cloud platforms (AWS, GCP, or Azure).
• Data & Messaging: Solid foundation in relational and NoSQL databases, along with event-driven architectures and message brokers (e.g., Kafka, RabbitMQ).
• Engineering Rigor: Strong architectural discipline regarding system scalability, fault tolerance, API design, CI/CD automation, and production monitoring.