Job
Description
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.
