AI/ML Engineer - Python/Django

Requirements :

- Over 5 years of hands-on experience in Machine Learning and Artificial Intelligence, including more than 1 year of direct involvement in Generative AI and Large Language Model (LLM) projects.

- Demonstrated success in deploying ML/AI systems into production environments.

- Experience collaborating with enterprise clients, with a strong understanding of their specific needs and challenges.

- Proficient in Python, with deep expertise across the ML ecosystem including Hugging Face, NumPy, Pandas, and scikit-learn.

- Practical production experience using deep learning frameworks such as PyTorch or TensorFlow, with a solid grasp of transformer architectures.

- Skilled in API development using FastAPI and deploying ML models to production.

- Proven knowledge of prompt engineering best practices and evaluation methodologies for LLMs.

- Experience working with vector databases like Pinecone or Weaviate and Retrieval-Augmented Generation

(RAG) pipelines.

- Familiarity with observability and monitoring tools for LLM-based applications.

- Knowledge of model optimization techniques such as compression, distillation, and large-scale deployment.

- Expertise in semantic search techniques and working with embedding models.

- Hands-on experience with LLM development frameworks such as LangChain and LlamaIndex, and familiarity with building agentic AI systems.

- Proficient in advanced model fine-tuning approaches including LoRA, prompt tuning, and adapter-based training.

Key Responsibilities:

- Develop, maintain, and optimize web applications using Python, Django/Flask.

- Design and implement RESTful APIs and integrate third-party services.

- Work with relational and non-relational databases such as PostgreSQL, MySQL, or MongoDB.

- Implement scalable backend architectures with security and performance best practices.

- Collaborate with frontend developers, designers, and other team members to ensure seamless application development.

- Develop and deploy AI/ML models if required, utilizing libraries such as TensorFlow, PyTorch, or Scikit-learn.

- Optimize applications for maximum performance, scalability, and efficiency.

- Debug and troubleshoot application issues and performance bottlenecks.

- Stay updated with the latest trends in Python development and AI/ML technologies.