Machine Learning & Generative AI Engineer

You are a Machine Learning & Generative AI Engineer responsible for designing, building, and deploying advanced ML and GenAI solutions. This role presents an exciting opportunity to work on cutting-edge AI systems, such as LLM fine-tuning, Transformer architectures, and RAG pipelines, while also contributing to traditional ML model development for decision-making and automation. With a minimum of 3 years of experience in Machine Learning, Deep Learning, and AI model development, you are expected to demonstrate a strong proficiency in Python, PyTorch, TensorFlow, Scikit-Learn, and MLflow. Your expertise should encompass Transformer architectures (such as BERT, GPT, T5, LLaMA, Falcon) and attention mechanisms. Additionally, experience with Generative AI, including LLM fine-tuning, instruction tuning, and prompt optimization is crucial. You should be familiar with RAG (Retrieval-Augmented Generation) embeddings, vector databases (FAISS, Pinecone, Weaviate, Chroma), and retrieval workflows. A solid foundation in statistics, probability, and optimization techniques is essential for this role. You should have experience working with cloud ML platforms like Azure ML / Azure OpenAI, AWS SageMaker / Bedrock, or GCP Vertex AI. Familiarity with Big Data & Data Engineering tools like Spark, Hadoop, Databricks, and SQL/NoSQL databases is required. Proficiency in CI/CD, MLOps, and automation pipelines (such as Airflow, Kubeflow, MLflow) is expected, along with hands-on experience with Docker and Kubernetes for scalable ML/LLM deployment. It would be advantageous if you have experience in NLP & Computer Vision areas, including Transformers, BERT/GPT models, YOLO, and OpenCV. Exposure to vector search & embeddings for enterprise-scale GenAI solutions, multimodal AI, Edge AI / federated learning, RLHF (Reinforcement Learning with Human Feedback) for LLMs, real-time ML applications, and low-latency model serving is considered a plus. Your responsibilities will include designing, building, and deploying end-to-end ML pipelines covering data preprocessing, feature engineering, model training, and deployment. You will develop and optimize LLM-based solutions for enterprise use cases, leveraging Transformer architectures. Implementing RAG pipelines using embeddings and vector databases to integrate domain-specific knowledge into LLMs will also be part of your role. Fine-tuning LLMs on custom datasets for domain-specific tasks and ensuring scalable deployment of ML & LLM models on cloud environments are critical responsibilities. Collaborating with cross-functional teams comprising data scientists, domain experts, and software engineers to deliver AI-driven business impact is expected from you.,