We are looking for a highly skilled AI/ML Engineer to design, build, and scale intelligent systems using Python, classical machine learning, and modern LLM-based architectures. This role focuses on production-grade developmentbuilding reliable, scalable ML-driven services and integrating them seamlessly into enterprise applications.

You will play a key role in system architecture, backend services, and model orchestration while ensuring clean, maintainable, and well-tested code.

Key Responsibilities :

- Design, develop, and maintain scalable, production-ready Python applications.

- Build and integrate ML and AI-driven services into backend systems.

- Develop and deploy classical ML models and LLM-based solutions.

- Implement LLM orchestration frameworks for prompt workflows, chaining, and inference pipelines.

- Write clean, reusable, and efficient code following industry best practices.

- Lead architecture decisions, framework selection, and system design for ML-enabled applications.

- Design and implement microservices-based architectures using REST APIs.

- Ensure smooth integration between frontend components, backend services, and ML pipelines.

- Design efficient data models to support analytics and ML workflows.

- Optimize applications for performance, scalability, and reliability.

- Troubleshoot and resolve complex technical and production issues.

- Implement unit tests, integration tests, and support CI/CD pipelines.

- Conduct code reviews and mentor team members on clean code and ML best practices.

Required Skills & Experience :

- Strong proficiency in Python for backend and ML development.

- Experience with classical machine learning techniques (regression, classification, clustering, etc.).

- Hands-on experience with LLM orchestration frameworks.

- Strong knowledge of SQL and relational data modeling.

- Experience building RESTful APIs and microservices.

- Solid understanding of clean code principles and software design patterns.

- Hands-on experience with Azure and/or GCP.

- Experience working with data processing libraries and ML pipelines.

- Understanding of deployment and scaling ML applications in cloud environments.