About Job/Role

Design, develop, and deploy scalable AI/ML models to solve complex business challenges.
Lead end-to-end ML workflows, including data preprocessing, model training, hyperparameter tuning, and deployment.
Apply classical machine learning techniques (e.g., boosting, ensemble methods) and modern deep learning frameworks (e.g., PyTorch, TensorFlow) to deliver innovative solutions.
Work with large datasets using tools such as PySpark, Databricks, Pandas, and Numpy to enable data-driven decision-making.
Develop and fine-tune Generative AI and large language models (LLMs) using tools like Hugging Face, LangChain, and similar frameworks.
Collaborate with cross-functional teams to integrate AI/ML solutions with existing systems and applications.
Leverage cloud platforms like Azure AI, Azure ML, Google Cloud Platform, and OpenAI to build and deploy AI/ML models at scale.
Utilize ML lifecycle management tools (e.g., MLflow) to track and monitor model performance.
Conduct thorough data analysis using SQL to derive actionable insights and optimize model performance.
Explore and implement conversational AI systems (e.g., Dialogflow, Vertex AI) to enhance user interactions.

Stay up-to-date with the latest advancements in AI/ML technologies and recommend their adoption where relevant.

Roles and Responsibilities

Design and develop foundational AI models and core components that power the organization's AI ecosystem, enabling scalable, reusable intelligence across products and services.
Optimize infrastructure including model architecture, training pipelines, and deployment framework to ensure high performance and reliability.
Collaborate with research, engineering, and product teams to translate cutting-edge AI advancements into production-ready capabilities.
Uphold ethical AI principles by embedding fairness, transparency, and accountability throughout the model development life cycle.

Required Qualification

Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field.
Prior experience in the healthcare domain, including working with clinical or patient datasets.
Hands-on experience with Generative AI applications and building conversational agents.
Strong understanding of data engineering principles, including ETL pipelines and data workflows.
Proven ability to handle unstructured datasets, such as text, images, and audio.
Excellent problem-solving and critical-thinking skills.
Strong communication skills and the ability to collaborate with cross-functional stakeholders.
Overall hands-on AI/ML project development using Python: 5+ years
Deep learning using PyTorch: 5+ years
Classical machine learning (e.g., sklearn, boosting, NLP): 5+ years
Data analysis using SQL: 5+ years
Working with large datasets using PySpark, Databricks, Pandas, Numpy, etc.: 2+ years
Experience with Transformers, Hugging Face, LLMs, Generative AI, LangChain: 2+ years
Proficiency with Azure AI, Azure ML, OpenAI: 2+ years
ML model deployment (e.g., MLflow, Databricks, Azure): 2+ years
Healthcare domain and system knowledge: 1+ year
Familiarity with Google Cloud Platform (e.g., Dialogflow, Vertex AI): 1+ year

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