Machine Learning Engineer

Key Responsibilities

• Generative AI & LLM Orchestration: Design and implement Retrieval-Augmented Generation (RAG) pipelines and Agentic workflows using frameworks like LangChain, LangGraph, or CrewAI.
• Model Optimization & Fine-tuning: Select, evaluate, and fine-tune open-source models (Llama 3, Mistral, etc.) using parameter-efficient techniques like LoRA/QLoRA.
• End-to-End ML/DL Development: Build and maintain traditional ML models (XGBoost, Scikit-learn) and Deep Learning architectures (PyTorch/TensorFlow) for classification, regression, and NLP tasks.
• MLOps & Deployment: Own the deployment of models into production using Docker and Kubernetes. Build automated CI/CD pipelines for ML (using tools like GitHub Actions, Kubeflow, or TFX).
• Evaluation & Observability: Implement rigorous evaluation frameworks for GenAI (e.g., RAGAS, LangSmith) to measure "faithfulness," "relevance," and "hallucination" rates.
• Data Infrastructure: Work with Vector Databases (Pinecone, Weaviate, or Milvus) to manage high-dimensional embeddings and optimize semantic search.
• Guardrails & Ethics: Implement safety guardrails (NeMo Guardrails, Llama Guard) to ensure PII redaction and prevent prompt injections or biased outputs.