Job highlights

- Required Skills Experience . 5+ years hands-on AI / ML development and deployment
- Preferred Skills .
- Experience in model training,fine-tuning,and production deployment at scale
- Experience in Life Sciences,Clinical Trials,Quality Systems
- Experience with multi-tenant SaaS AI systems

Key Responsibilities

- Design and implement robust AI pipelines for workflows such as Document Lifecycle, CAPA, Deviations, Training Evaluation, and Risk Prediction.
- Train, fine-tune, and deploy ML/DL models for document intelligence (SOP extraction, version comparison, multilingual analysis), AI-powered regulatory search, audit assistants, and training solutions.
- Build advanced risk analytics for predictive modeling (deviations, CAPA effectiveness, process bottlenecks) using Azure Machine Learning.
- Implement AI-driven LMS features: quiz evaluation, training prediction, adaptive learning recommendations.
- Deploy, monitor, and maintain AI/ML models using Azure Machine Learning (pipelines, endpoints, versioning).
- Leverage Azure Cognitive Services for text analytics, translation, anomaly detection.
- Implement vector embeddings/semantic search with Azure AI Search for compliance data.
- Integrate Azure OpenAI for audit prep, natural language QA, regulatory chatbots.
- Collaborate with QMS/LMS teams to advance automation in regulated environments.
- Partner with compliance, QA, and regulatory teams to ensure full statutory adherence.
- Mentor junior AI engineers and data scientists in best practices.
- Maintain data compliance (21 CFR Part 11, GDPR), data lineage,



traceability and audit trails.
- Ensure explainable AI with full documentation for regulatory transparency.

Required Skills Experience

- 5+ years hands-on AI/ML development and deployment.
- Strong experience with Azure AI stack:
- Azure Machine Learning (pipelines, ML-Ops, model registry, monitoring)
- Azure AI Search (vector DB, embeddings, semantic ranking)
- Azure OpenAI (prompt engineering, fine-tuning)
- Azure AI Services (translation, text analytics, anomaly detection)
- Azure Document Intelligence (OCR, form recognition, entity extraction)

- Strong programming in Python (PyTorch, TensorFlow, scikit-learn).
- ML-Ops best practices (CI/CD for ML, model lifecycle, drift detection).
- Data engineering pipelines (Azure Data Factory, Event Grid, Databricks).
- Experience in model training, fine-tuning, and production deployment at scale.
- Solid knowledge of regulatory compliance (21 CFR Part 11, GDPR) for AI systems.
- Familiarity with LLM and embedding model integration in enterprise apps.

Preferred Skills

- Experience in Life Sciences, Clinical Trials, Quality Systems.
- Experience with multi-tenant SaaS AI systems.
- Solid understanding of Responsible AI principles.
- Proven work with data engineers to fine tune data pipelines (Azure Data Factory, Event Grid).
- Compliance for data storage, audit trails, model versioning.
- Able to implement explainability features for regulatory AI.
- Track record of maintaining audit-ready model documentation (training data lineage, version control, performance metrics)

📌 AI Platform Engineer (Gonda)
🏢 Infiligence
📍 Gonda