About the role
The core responsibilities for the job include the following:
Core ML and Engineering:
- Write clean, efficient, and well-documented Python code following OOP principles (encapsulation, inheritance, polymorphism, abstraction).
- Build and manage end-to-end ML pipelines: data ingestion, preprocessing, model training, evaluation, and deployment.
- Develop scalable ML systems using frameworks like PyTorch, TensorFlow, and Scikit-learn.
Generative AI (GenAI) and LLM Systems:
- Design and implement LLM-based applications (chatbots, copilots, automation tools).
- Build and optimize RAG pipelines using vector databases (e. g., FAISS, Pinecone, Weaviate).
- Develop agentic workflows using frameworks like LangChain, LlamaIndex, or similar.
- Implement prompt engineering, structured output generation, and tool/function calling.
- Fine-tune or optimize LLMs using techniques like LoRA, QLoRA, or instruction tuning.
- Work with open-source and proprietary LLMs (e. g., LLaMA, Mistral, GPT, Qwen).
Software Design and Architecture:
- Design modular, scalable, and maintainable ML and GenAI systems.
- Build APIs and microservices for model serving and GenAI applications.
- Contribute to architectural decisions for AI platforms and products.
Data Engineering for AI:
- Build data pipelines for feature engineering, transformation, and dataset versioning.
- Manage structured and unstructured data (documents, embeddings, logs).
MLOps and LLMOps:
- Implement CI/CD pipelines for ML and GenAI systems.
- Manage model and prompt versioning, experiment tracking, and reproducibility.
Requirements:
- Bachelor's or Master's degree in Computer Science, AI, ML, Data Science, or a related field.
- 5 years of experience in AI/ML engineering, with 2-3 years in a lead role.
- Strong expertise in Python, system design, and scalable AI/ML architecture.
- Hands-on experience with TensorFlow, PyTorch, and Scikit-learn.
- Strong knowledge of NLP, Computer Vision, Generative AI, LLMs, and deep learning models.
- Experience with Docker, Kubernetes, MLOps, CI/CD, and cloud platforms like Amazon Web Services, Google Cloud Platform, or Microsoft Azure.
- Strong leadership, stakeholder management, and team mentoring skills.