Sr. AI Engineer

About the Role We are seeking a skilled and hands-on Sr. AI Engineer with 7–9 years of experience in developing, fine-tuning, and deploying machine learning and deep learning models, including Generative AI systems. The ideal candidate has a strong foundation in classification, anomaly detection, and time-series modeling, along with experience in Transformer-based architectures. Expertise in model optimization, quantization, and Retrieval-Augmented Generation (RAG) pipelines is highly desirable. Responsibilities Design, train, and evaluate ML models for classification, anomaly detection, forecasting, and natural language understanding tasks. Build and fine-tune deep learning models, including RNNs, GRUs, LSTMs, and Transformer architectures (e.g., BERT, T5, GPT). Develop and deploy Generative AI solutions, including RAG pipelines for applications such as document search, Q&A, and summarization. Apply model optimization techniques, including quantization, to improve latency and reduce memory/compute overhead in production. Fine-tune large language models (LLMs) using Supervised Fine-Tuning (SFT) and Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA or QLoRA (optional). Define, track, and report relevant evaluation metrics; monitor model drift and retrain models as required. Collaborate with cross-functional teams (data engineering, backend, DevOps) to productionize ML models using CI/CD pipelines. Maintain clean, reproducible code, and proper documentation and versioning of experiments. Required Skills & Qualifications 7–9 years of hands-on experience in machine learning, deep learning, or data science roles. Proficiency in Python and ML/DL libraries: scikit-learn, pandas, PyTorch, TensorFlow. Strong understanding of traditional ML and deep learning, particularly for sequence and NLP tasks. Experience with Transformer models and open-source LLMs (e.g., Hugging Face Transformers). Familiarity with Generative AI tools and RAG frameworks (e.g., LangChain, LlamaIndex). Experience in model quantization (dynamic/static, INT8) and deploying models in resource-constrained environments. Knowledge of vector stores (e.g., FAISS, Pinecone, Azure AI Search), embeddings, and retrieval techniques. Proficiency in evaluating models using statistical and business metrics. Experience with model deployment, monitoring, and performance tuning in production. Familiarity with Docker, MLflow, and CI/CD practices. Preferred Qualifications Experience fine-tuning LLMs (SFT, LoRA, QLoRA) on domain-specific datasets. Exposure to MLOps platforms (e.g., SageMaker, Vertex AI, Kubeflow). Familiarity with distributed data processing frameworks (e.g., Spark) and orchestration tools (e.g., Airflow). Contributions to research papers, blogs, or open-source projects in ML/NLP/Generative AI.