Qualifications

• 2–5 years of hands-on Python development experience with solid OOP principles
• Practical experience with machine learning and deep learning frameworks (PyTorch, TensorFlow, scikit-learn)
• Basic to intermediate understanding of LLM concepts
• Experience in preparing and processing datasets (Pandas, NumPy, SQL)
• Ability to build and consume REST APIs (FastAPI or similar)
• Familiarity with Git, branching strategies, and general software engineering best practices
• Basic understanding of Kubernetes concepts
• Comfort working in Linux environments
• Awareness of modern cloud-native technologies
• (microservices, message queues, distributed systems, monitoring)
• Ability to analyze distributed system behavior (logs, traces, metrics)
• Research-driven mindset and willingness to experiment and validate new AI methods
• Ability to write clean, maintainable, and well-documented code

Responsibilities

• Develop, train, evaluate, and optimize ML/AI models
• Integrate machine learning or LLM models into applications and microservices
• Manage the full model lifecycle: experimentation → validation → deployment
• Build and maintain data pipelines for model training and inference
• Develop and consume REST APIs for AI-powered services
• Monitor, log, and troubleshoot model performance in production environments
• Work with containerized deployments and contribute to scalable infrastructure design
• Collaborate with cross-functional teams to deliver AI-driven solutions
• Ensure clean documentation, technical clarity, and maintain best coding practices
• Continuously research emerging AI technologies and contribute to model improvements

Preferred Skills & Certifications

• LLM fine-tuning (LoRA, QLoRA) or prompt engineering
• High-performance model serving (vLLM, TGI, etc.)
• Basic understanding of GPU acceleration (CUDA)
• Experience with microservice architectures and async processing