Project info:

Out next-generation platform initiative, designed to redefine professional services delivery through agent-enabled and agent-driven models. The project’s goal is to create an “always-on, insight-rich, and value-transparent” ecosystem that adapts to evolving client and market needs. By leveraging AI-powered automation and knowledge reuse, Eclipse aims to scale trusted outcomes, expand service reach, and protect margins in a rapidly commoditizing market.

About the Role
We’re looking for a Sr. Python Engineer to join a fast-moving team building a production-grade, AI-native platform. You’ll focus on the architecture and orchestration of our AI systems, building robust agentic pipelines, MCP servers, and seamlessly integrating LLMs with our backend infrastructure.

Tech Stack Highlights

·       Languages & Frameworks: Python 3, FastAPI, Celery

·       Storage: PostgreSQL, Redis, Azure Blob Storage

·       Messaging & Cache: RabbitMQ, Azure Service Bus

·       Cloud: Azure (Service Bus, Blob Storage, Key Vault)

·       Tooling: Docker, Claude Code, Cursor, Spec Driven Framework, Databricks

What We’re Looking For

Must-Have

·       Demonstrates resilience and adaptability in high-pressure environments while consistently delivering on tight timelines

·       Proven ability to design and architect scalable, highly concurrent backend systems, distributed task queues, and resilient API services

·       Strong Python fundamentals – async/await, type annotations, Pydantic

·       Hands-on FastAPI experience building production REST services

·       Hands-on experience with databricks (databricks Job, Delta Lake.etc)

·       Deep experience with Celery – designing and operating distributed task queues, background workers, and worker scaling strategies with Redis or RabbitMQ

·       Working knowledge of PostgreSQL database and SQL

·       Comfortable deploying on Azure, working with Docker, and architecting deployments to utilize all available cores inside the container

Nice-to-Have

·       Datadog for system observability

·       Kubernetes experience

·       Familiarity with AI tooling integration (serving as the backend support for AI features)

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