AI / ML Engineer

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.

Skills

genai, generative ai, large language model, llm, python, pytorch, scikit-learn, tensorflow