About the Role
We are seeking a Senior Python & AI Engineer to join a fast-paced technology team delivering advanced data and AI solutions in an enterprise environment. This role combines strong software engineering, data processing, and applied AI expertise, with a focus on building production-grade Python services, anomaly detection solutions, and generative AI applications that address real business challenges.
Main Responsibilities
- Design, develop, and maintain production-grade Python data processing pipelines, APIs, and AI/ML services.
- Build and deploy FastAPI-based AI service endpoints and asynchronous processing systems for enterprise applications.
- Develop anomaly detection and analytics solutions using techniques such as isolation forest, clustering, time-series analysis, and pattern mining.
- Design and implement generative AI solutions leveraging LLMs and multimodal models to support business use cases.
- Collaborate with cross-functional teams to define requirements, integrate AI solutions, and deliver scalable production-ready systems.
- Ensure software quality through unit testing, code reviews, version control, monitoring, logging, and continuous improvement practices.
Qualifications
- 5–7 years of hands-on experience in Python development and database technologies, including SQL query development and stored procedures.
- 2–3 years of practical experience delivering AI or machine learning projects in production environments.
- Strong expertise in pandas, scikit-learn, FastAPI, object-oriented programming (OOP), and production AI service development.
- Solid understanding of anomaly detection methods, machine learning workflows, model monitoring, debugging, and logging practices.
- Experience with Unix/Linux environments, Git version control, unit testing frameworks (e.g., pytest), and modern software engineering practices.
- Ability to work independently in a fast-paced environment, manage multiple priorities, and collaborate effectively within Agile teams; experience with financial data and containerized ML deployments is considered an asset.
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