Sr AI Engineer (Generative AI & Pharmacovigilance)

We are seeking a highly motivated AI Engineer with 5+ years of experience in Artificial Intelligence, Machine Learning, Generative AI, and Agentic Engineering to join our Pharmacovigilance Technology team.

The ideal candidate will work closely with Pharmacovigilance SMEs, Product Owners, Data Scientists, Safety Operations Teams, and Software Engineers to design, develop, and deploy AI-powered solutions that enhance drug safety monitoring, adverse event case processing, signal detection, literature surveillance, regulatory reporting, and medical document intelligence.

This role offers an opportunity to shape next-generation AI products leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), NLP, Machine Learning, and Agentic AI frameworks within the Life Sciences domain.


ESSENTIAL DUTIES AND RESPONSIBILITIES

Our employees are tasked with delivering excellent business results through the efforts of their teams. These results are achieved by:

AI Solution Development

• Design, develop, and deploy AI/ML solutions for Pharmacovigilance business processes.
• Build Generative AI applications using OpenAI, Azure OpenAI, Anthropic, Llama, or equivalent LLM platforms.
• Develop domain-specific AI assistants for PV operations and safety case management.
• Build intelligent document processing solutions for source documents, ICSRs, safety narratives, and regulatory reports.

Large Language Models GenAI

• Build and optimize RAG-based applications using vector databases.
• Develop prompt engineering frameworks and evaluation methodologies.
• Fine-tune domain-specific models using pharmacovigilance datasets.
• Develop AI agents and workflow automation capabilities using Agentic AI frameworks.
• Develop and implement evaluation strategies for LLM and Agentic AI applications.

Data Engineering Integration

• Collaborate with data engineers to integrate safety systems and clinical data sources.
• Develop data pipelines for structured and unstructured PV data.
• Integrate APIs and enterprise applications into AI workflows.
• Work with structured and graph-based data sources to support advanced AI applications.

MLOps Deployment

• Deploy AI models and GenAI applications into production environments.
• Implement monitoring, model evaluation, drift detection, and performance optimization.
• Maintain scalable, secure, and compliant AI infrastructure.
• Implement observability and telemetry for AI/ML applications and services using OpenTelemetry.
• Support CI/CD and automated deployment pipelines for AI applications.

Compliance Governance

• Ensure AI solutions comply with GxP, GVP, FDA, EMA, MHRA, and internal quality standards.
• Support AI validation, audit readiness, traceability, and documentation requirements.
• Implement Responsible AI and model governance practices.

Stakeholder Collaboration

• Partner with Pharmacovigilance SMEs and Product Managers to understand business requirements.
• Translate regulatory and safety requirements into scalable AI solutions.
• Support demos, proof-of-concepts, and innovation initiatives. Qualifications

MINIMUM KNOWLEDGE, SKILLS AND ABILITIES

The requirements listed below are representative of the experience, education, knowledge, skill and/or abilities required.

Education

• Bachelor s degree in Computer Science, Data Science, Artificial Intelligence, Engineering, Physics, Bioinformatics, or a related discipline.
• Master s or PhD in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Physics, Engineering, Bioinformatics, or a related discipline is highly valued.
• Candidates with advanced degrees are encouraged to apply; however, 5+ years of relevant hands-on industry experience remains the primary experience requirement.

Experience

• 5+ years of hands-on experience in AI/ML engineering.
• Experience developing and deploying production-grade AI applications.
• Mandatory experience developing solutions using Agentic AI frameworks.
• Experience with Generative AI, LLMs, RAG, NLP, and AI/ML application development.
• Experience working with cloud platforms and production AI/ML deployment environments.
• Experience working in Healthcare, Life Sciences, Clinical, or Pharmacovigilance domains is preferred.

Technical Skills

Programming / Databases

• Python - Mandatory
• SQL
• REST APIs
• PostgreSQL

AI / ML

• Machine Learning
• Deep Learning
• Transformer Models
• Generative AI
• LLM Fine-Tuning
• NLP

GenAI Ecosystem

• Azure OpenAI / OpenAI APIs
• LangChain
• Agentic AI frameworks - Mandatory
• crewAI
• LlamaIndex
• Prompt Engineering
• RAG Architecture
• Semantic Search
• Vector Databases

Cloud Platforms

• GCP - Preferred
• Azure
• AWS

MLOps / Infrastructure

• MLflow
• Docker
• Kubernetes
• CI/CD Pipelines
• OpenTelemetry

PREFERRED QUALIFICATIONS

• Good understanding of Pharmacovigilance processes such as ICSR intake, case processing, submission, aggregate reports, signal detection, etc.
• Experience with Graph Databases and GraphRAG.
• Knowledge of Clinical Trial and Regulatory ecosystems.
• Experience working in GxP-validated environments.
• Experience implementing AI solutions within regulated Healthcare or Life Sciences environments.
• Experience with AI observability, evaluation, monitoring, and model governance.

Preferred Domain Knowledge

• Pharmacovigilance
• Drug Safety
• Clinical Research
• Clinical Trials
• Regulatory Affairs
• Life Sciences
• Healthcare
• GxP / GVP environments
• FDA / EMA / MHRA regulatory ecosystems