AI Engineer

Design, develop, and deploy machine learning and deep learning models for classification, regression, clustering, forecasting, anomaly detection, and predictive analytics use cases.

• Build and optimize feature engineering pipelines and data preprocessing workflows for structured and unstructured datasets.

• Perform exploratory data analysis (EDA) to identify trends, patterns, and actionable insights from large-scale healthcare and business datasets.

• Develop and evaluate models using appropriate metrics, cross-validation, and experimentation frameworks.

• Implement NLP solutions for text classification, entity extraction, summarization, and information retrieval.

• Design and fine-tune Large Language Models (LLMs) and transformer-based architectures for domain-specific applications.

•Apply transfer learning and prompt engineering techniques to adapt foundation models to business and healthcare use cases.

• Build AI-driven document intelligence solutions using models such as LayoutLM, Donut, and Table Transformers for key-value extraction and document understanding.

• Support MLOps and model deployment, including Docker-based packaging, cloud deployment, monitoring, and performance optimization.

•Collaborate with product, engineering, and business teams to translate business problems into AI/ML and GenAI solutions.

•Research and experiment with emerging AI technologies, including RAG (Retrieval-Augmented Generation), LLM alignment techniques (RLHF, DPO, PPO, KTO), and model optimization methods.

Requirements

•5+ years of hands-on experience in AI/ML model development and deployment.

•Strong understanding of machine learning, deep learning, neural network architectures, training methodologies, and optimization algorithms.

•Proficiency in Python and AI/ML libraries such as PyTorch, TensorFlow, Scikit-Learn, Pandas, and NumPy.

•Experience with NLP libraries such as Hugging Face Transformers, spaCy, and NLTK.

•Hands-on experience with LLM fine-tuning, prompt engineering, and transformer-based models.

•Experience with SQL and/or NoSQL databases and data manipulation at scale.

•Knowledge of cloud platforms (AWS/Azure), Docker, and ML deployment workflows.

•Strong analytical, problem-solving, and communication skills, with the ability to explain complex AI concepts to technical and non-technical stakeholders.

Preferred Skills:

•Experience with RAG architectures, vector databases, embeddings, and semantic search.

•Familiarity with LLM alignment techniques such as RLHF, DPO, PPO, and KTO.

•Experience with computer vision or document AI models for OCR and document understanding.

•Knowledge of Airflow or other workflow orchestration tools.

•Experience working with HIPAA, PHI, PII, or GDPR-compliant systems.

•Familiarity with Linux, Git, Jupyter Notebooks, and Agile development practices.

•Experience with distributed computing and scalable AI infrastructure is a strong advantage.