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Tier-1 brand, mid-level ML role, metro location, and broad LLM/MLOps requirements.
ML/LLM skills transferable but GxP/regulatory knowledge is industry-specific.
Explicit years plus specialized LLM, MLOps and GxP compliance requirements.
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Design, build, and deploy generative AI and Large Language Model (LLM) applications using frameworks like LangChain and LlamaIndex.
Develop reusable prompts and evaluation metrics for LLMs (e.g., OpenAI GPT-4, Anthropic Claude) and manage data ingestion into vector databases.
Ensure AI/ML solutions comply with GxP and regulatory standards while collaborating with cross-functional teams to support quality and regulatory inspection readiness.
Master's degree in related fields such as Software Engineering, Data Science, or ML Engineering.
2–4 years of experience developing and deploying LLM applications with strong skills in Python and ML libraries (TensorFlow, PyTorch, Scikit-learn).
Experience with MLOps tools (e.g., MLflow, CI/CD), cloud platforms (AWS, Azure, GCP), and knowledge of GxP compliance in regulated environments.
Work Experience Required: 2–4 years relevant experience in software/data science roles focused on AI/ML and LLM deployment.
Technically strong candidate with hands-on experience in generative AI, LLM prompt engineering, and MLOps pipelines in regulated (GxP) environments.
Comfortable working in an Agile (SAFe) global team environment collaborating across engineering, business, and quality functions.
Experienced in applying cloud and data engineering tools (e.g., Spark, Databricks) to build scalable AI-driven quality solutions aligned with regulatory compliance.