Description :

Job Summary :

We are seeking a highly skilled and versatile Machine Learning Engineer (with GenAI expertise) who combines strong software engineering fundamentals with deep experience in machine learning and modern Generative AI systems. The ideal candidate will design, develop, and maintain scalable AI-powered applications using Python, with a strong emphasis on Object-Oriented Programming principles.

You will play a key role in building end-to-end AI systems, including LLM-powered applications, Retrieval-Augmented Generation (RAG) pipelines, and agent-based workflows, alongside traditional ML models. This role demands strong analytical thinking, coding expertise, architectural design skills, and a deep understanding of the full ML and GenAI lifecyclefrom data processing and model development to deployment, monitoring, and optimization.

Key Responsibilities :

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.