AI Team Lead

·       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

·       9+ 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.