About Us
We’re a fast-growing product company integrating cutting-edge AI capabilities into our core offering to stay competitive and deliver exceptional value to customers. Our AI work spans task-specific ML models, large language model (LLM) integration, and agentic systems that orchestrate multiple tools to produce end-user results.
We run a Python-based backend (FastAPI + Gunicorn + Nginx) with heavy background job processing using Celery. We’re looking for a senior-level AI Engineer who is equally strong in backend engineering and applied AI — capable of building production-grade systems that are fast, reliable, and maintainable.
What You’ll Do
- Design, develop, and deploy production-grade AI-powered backend systems.
- Integrate LLMs and traditional ML models into performant, scalable architectures.
- Integrate and optimize vector databases for retrieval-augmented generation (RAG) pipelines and other traditional ML queries.
- Write clean, well-structured, and testable Python code following best practices.
- Capable of thinking about performance and ensuring optimal decision making to reduce latency.
- Build hybrid architectures that balance LLM calls with traditional ML.
- Debug complex, cross-layer issues spanning backend, AI inference, and UI integration.
- Conduct thorough dev testing before QA handoff to ensure production reliability.
- Collaborate with product, backend, and frontend engineers to deliver cohesive solutions.
Must-Have Skills & Experience
- 3–5+ years professional backend engineering experience in Python, FastAPI or Flask, and background processing.
- Masters Degree in related field
- Proven record of deploying Python applications to production (not just scripts or academic work).
- Strong grasp of software design patterns
- Strong understanding of backend performance, parallel processing in background jobs and multi-threading
- Proficiency in performance tuning specially for heavy AI models
- Applied machine learning experience — training, evaluating, and maintaining small task-specific models.
- Familiarity with LLM integration, prompt engineering, and context window optimization.
- Proven ability to debug AI behavior, identify root causes, and make targeted fixes.
- Strong testing discipline for both backend and AI components.
