Sr AI Engineer (Generative AI & Pharmacovigilance)

Accountabilities:

• • Design, develop, and deploy AI and machine learning solutions that improve pharmacovigilance and drug-safety processes.
  • Build Generative AI applications using platforms and models such as OpenAI, Azure OpenAI, Anthropic, Llama, or equivalent technologies.
  • Develop domain-specific AI assistants and intelligent workflows supporting pharmacovigilance operations and safety case management.
  • Create intelligent document-processing solutions for source documents, Individual Case Safety Reports (ICSRs), safety narratives, and regulatory submissions.
  • Architect and optimize Retrieval-Augmented Generation (RAG) applications using vector databases, semantic search, and relevant retrieval technologies.
  • Develop prompt-engineering frameworks, LLM evaluation methodologies, and domain-specific model fine-tuning approaches.
  • Design, implement, and evaluate AI agents and workflow automation using Agentic AI frameworks.
  • Build and maintain data pipelines that integrate structured and unstructured pharmacovigilance, clinical, and safety data.
  • Integrate APIs, enterprise applications, and data sources into scalable AI workflows, including structured and graph-based data.
  • Deploy AI models and applications into production while implementing monitoring, evaluation, drift detection, performance optimization, and observability.
  • Implement scalable and secure AI infrastructure, CI/CD pipelines, automated deployments, and telemetry using tools such as OpenTelemetry.
  • Ensure AI solutions meet applicable GxP, GVP, FDA, EMA, MHRA, and internal quality and compliance requirements.
  • Support AI validation, audit readiness, traceability, documentation, Responsible AI, and model-governance practices.
  • Partner with pharmacovigilance SMEs and product leaders to translate business, safety, and regulatory requirements into scalable technical solutions.
  • Contribute to demonstrations, proof-of-concepts, innovation initiatives, and the continuous evolution of AI capabilities across the life sciences domain.

  Requirements:

  • 5+ years of hands-on experience in AI/ML engineering, with a strong track record of developing and deploying production-grade AI applications.
  • Mandatory experience developing solutions using Agentic AI frameworks, alongside practical experience with Generative AI, LLMs, RAG, NLP, and machine learning.
  • Strong Python programming skills, with experience in SQL, REST APIs, and PostgreSQL.
  • Hands-on knowledge of machine learning, deep learning, transformer models, NLP, Generative AI, and LLM fine-tuning.
  • Experience with GenAI technologies and frameworks such as Azure OpenAI, OpenAI APIs, LangChain, LlamaIndex, crewAI, prompt engineering, RAG architecture, semantic search, and vector databases.
  • Experience deploying AI/ML solutions on cloud platforms, with knowledge of Azure and AWS; GCP experience is preferred.
  • Practical experience with MLOps and production infrastructure, including MLflow, Docker, Kubernetes, CI/CD pipelines, and AI observability.
  • Experience with AI evaluation, monitoring, model performance optimization, telemetry, and governance.
  • Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, Physics, Bioinformatics, or a related field; a Master's or PhD is highly valued.
  • Strong preference for candidates with experience in healthcare, life sciences, clinical research, or pharmacovigilance environments.
  • Knowledge of pharmacovigilance processes such as ICSR intake, case processing, regulatory submission, aggregate reporting, and signal detection is highly desirable.
  • Experience with graph databases, GraphRAG, clinical-trial ecosystems, regulatory systems, or GxP-validated environments is an advantage.
  • Understanding of FDA, EMA, MHRA, GxP, and GVP requirements is valuable for working effectively in regulated environments.
  • Strong analytical, problem-solving, communication, and stakeholder-management skills, with the ability to collaborate effectively across technical, product, safety, and regulatory teams.
  • A proactive, innovative mindset and willingness to take ownership of complex AI engineering challenges while maintaining a strong focus on quality, patient impact, and responsible technology.

  Benefits:

  • Opportunity to work on high-impact AI solutions supporting drug safety, pharmacovigilance, healthcare, and life sciences.
  • Exposure to cutting-edge technologies including Generative AI, LLMs, Agentic AI, RAG, NLP, GraphRAG, and advanced machine learning.
  • Opportunity to work with cross-functional teams spanning AI engineering, data science, pharmacovigilance, product management, safety operations, and software engineering.
  • Experience developing AI products within regulated healthcare and life sciences environments.
  • Opportunities to deepen expertise in responsible AI, model governance, AI observability, validation, and compliant production deployment.
  • Collaborative and inclusive culture that emphasizes innovation, professional development, transparent communication, and teamwork.
  • Opportunities to contribute to meaningful technology initiatives focused on improving patient outcomes and advancing drug safety.
  • Global working environment with exposure to diverse teams, clients, technologies, and life sciences challenges.
  • Professional growth opportunities through hands-on ownership of advanced AI initiatives and emerging technology capabilities.