Senior AI/ML Engineer

• AI/ML Model Development: Design, build, and optimize machine learning models for real-world use cases, Develop models for prediction, classification, recommendation, and NLP tasks, Build end-to-end ML pipelines for training, validation, and evaluation
• Generative AI & LLM Development: Develop applications using Large Language Models (LLMs), Build and maintain RAG (Retrieval-Augmented Generation) pipelines, Implement prompt engineering, embeddings, vector search, and semantic retrieval, Fine-tune LLMs when required
• Production Deployment: Deploy ML models into production using scalable microservices and APIs, Build inference pipelines with high performance and low latency, Implement monitoring, logging, and model performance optimization
• Data Engineering Collaboration: Work with data engineering teams to build robust ML training and inference pipelines, Ensure clean, high-quality datasets and feature engineering workflows
• AI Platform & Architecture: Design ML system architecture for high-volume, low-latency environments, Integrate AI/ML solutions into enterprise-grade applications
• Research & Innovation: Stay updated on the latest advancements in AI, ML, and Generative AI, Evaluate and experiment with new frameworks, LLMs, and tools

• Strong proficiency in Python
• Hands-on experience with ML libraries/frameworks: TensorFlow, PyTorch, Scikit-learn, Hugging Face
• Machine Learning Expertise: Strong understanding of Deep learning, Feature engineering, Model evaluation techniques, Model optimization
• NLP & LLM Experience: Working experience with transformer models and LLM APIs, Expertise in prompt engineering, embeddings, and vector databases, Familiarity with tools/technologies such as OpenAI / Claude / Llama, FAISS, Pinecone, Weaviate, Chroma
• MLOps & Deployment: Experience with Docker, Kubernetes, CI/CD for ML pipelines, Model monitoring and versioning, Hands-on with tools like MLflow, Kubeflow, Airflow
• Cloud Platforms: Hands-on experience with at least one major cloud platform: AWS, Azure, GCP, Experience with cloud AI services such as SageMaker, Azure ML, or Vertex AI

Experience with distributed training or large-scale model optimization