- Develop enterprise-level GenAI applications using LLM frameworks such as Langchain, Autogen, and Hugging Face.
- Design and develop intelligent pipelines using PySpark, TensorFlow, and PyTorch within Databricks and AWS environments.
- Implement embedding models and manage VectorStores for retrieval-augmented generation (RAG) solutions.
- Integrate and leverage MDM platforms like Informatica and Reltio to supply high-quality structured data to ML systems.
- Utilize SQL and Python for data engineering, data wrangling, and pipeline automation.
- Build scalable APIs and services to serve GenAI models in production.
- Lead cross-functional collaboration with data scientists, engineers, and product teams to scope, design, and deploy AI-powered systems.
- Ensure model governance, version control, and auditability aligned with regulatory and compliance expectations.
- Guide the team on development activities and lead solution discussions.
Basic Qualifications and Experience:
- Master's degree with 4 - 6 years of experience in Business, Engineering, IT, or related field OR
- Bachelor's degree with 6 - 9 years of experience in Business, Engineering, IT, or related field OR
- Diploma with 10 - 12 years of experience in Business, Engineering, IT, or related field
Functional Skills:
Must-Have Skills:
- 6+ years of experience working in AI/ML or Data Science roles, including designing and implementing GenAI solutions.
- Hands-on experience with LLM frameworks and tools like Langchain, Autogen, Hugging Face, OpenAI APIs, and embedding models.
- Strong programming background in Python, PySpark, TensorFlow, PyTorch, and SK-Learn.
- Proven experience building and deploying AI/ML applications in cloud environments such as AWS.
- Expertise in developing APIs, automation pipelines, and serving GenAI models using Django, FastAPI, and DataBricks.
- Solid experience integrating and managing MDM tools (Informatica, Reltio) and applying data governance best practices.
- Core technical capabilities in GenAI and Data Science.
Good-to-Have Skills:
- Experience in Data Modeling, ETL development, and data profiling to support AI/ML workflows.
- Knowledge of Life Sciences or Pharma industry standards and regulatory considerations.
- Proficiency in Agile tools like JIRA and Confluence.
- Familiarity with MongoDB, VectorStores, and scalable GenAI architecture principles.
Professional Certifications:
- ETL certification (e.g., Informatica)
- Data Analysis certification (SQL)
- Cloud certification (AWS or Azure)
- Data Science and ML certifications
Soft Skills:
- Strong analytical skills for assessing and improving master data processes.
- Excellent verbal and written communication skills for conveying complex data concepts.
- Effective problem-solving skills to implement scalable solutions.
- Ability to work effectively with global and virtual teams.
