Senior Software Engineer - Backend & AI Infra

Senior Software Engineer - Backend & Ai Infra Focus CVector's mission is to bring real time economic optimization and AI prediction to every energy and manufacturing plant. Industrial facilities make decisions every minute that determine cost, reliability, and margin, but the signals that matter live in different worlds: live asset constraints and process reality on one side, feedstock prices, product prices, demand, and market dynamics on the other. We fuse those worlds into one decision layer that continuously forecasts what is coming, simulates what could happen, and optimizes what to do next, so plants can run closer to their true economic potential every day. This position works from our New York City office four days per week. CVector has customers across the United States and operates real-world systems in demanding industrial environments. Role Overview As a Senior Software Engineer - Backend & AI Infra focus, you will play a critical role in evolving CVector's core backend platform. You will work on time-series data systems, AI-assisted analytics, cloud infrastructure, and data ingestion pipelines that power our customer-facing applications and internal modeling platforms. This role is well-suited for an engineer who enjoys working close to the data and infrastructure layers, has strong architectural judgment, and is excited to operate across AI systems, databases, and distributed backend services. You will take ownership of complex systems, drive major technical migrations, and help shape how intelligence is embedded into industrial energy workflows. You will collaborate closely with product, modeling, and frontend engineers, and you will have significant influence over platform direction, reliability, and long-term scalability. Key Responsibilities As a Senior Software Engineer, you will contribute across several interconnected areas: Intelligent Systems Map customer domains and operational workflows into effective prompts and AI system interfaces Design, execute, and iterate on evals for AI outputs Incorporate customer feedback and reinforcement signals to improve system behavior Refine context selection, retrieval, and trace collection to improve output quality Fine-tune smaller models using collected traces to reduce latency while preserving performance Evaluate and integrate new AI platforms and models as they become available Support training and deployment of large, time-series-focused models Backend Platform & Data Infrastructure Lead migrations and upgrades of our time-series data schemas and storage engines Upgrade and maintain PostgreSQL and related database infrastructure Develop and maintain data connectors for industrial and third-party systems Lead MQTT-based data ingestion pipeline improvements Transition PostgREST to GraphQL-based framework and evolve our API architecture Improve TigerData to next-gen time series design and simplify multi-tenant provisioning workflows Database & Analytics Systems Optimize performance and reliability of high-volume time-series data stores Design and execute architecture plans for TigerData Augment analytical workloads using Parquet and/or Iceberg-based storage formats Balance real-time and historical query performance across operational and analytical use cases Modeling Platform Support Improve and consolidate internal machine learning systems Enable parallelized and distributed model training workflows Implement message brokers and orchestration mechanisms for multi-stage learning pipelines Improve reproducibility, traceability, and coordination across modeling stages Reliability & Developer Experience Strengthen cloud infrastructure uptime, observability, and deployment reliability Standardize build, test, and release processes across services Improve developer tooling and internal platform ergonomics Port backend services from bun to Node.js where appropriate Track and improve DORA metrics (deployment frequency, lead time, change failure rate, recovery time) Participate in an on-call rotation and continuously improve operational readiness