Senior AI/ML Engineer

• Core ML & 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) & 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 & 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 & LLMOps

• Implement CI/CD pipelines for ML and GenAI systems.

• Manage model and prompt versioning, experiment tracking, and reproducibility.

• Monitor systems for performance, drift, hallucinations, latency, and cost.

• Implement guardrails, evaluation frameworks, and feedback loops for LLMs.

• Performance & Scalability

• Optimize inference latency and cost for ML and LLM systems.

• Ensure scalability under production workloads (batch + real-time).

• Documentation

• Create clear documentation for ML models, GenAI pipelines, APIs, and workflows.