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 .