Sr AI Engineer (Generative AI & Pharmacovigilance)

At EVERSANA, we are proud to be certified as a Great Place to Work across the globe. We’re fueled by our vision to create a healthier world. How? Our global team of more than 7,000 employees is committed to creating and delivering next-generation commercialization services to the life sciences industry. We are grounded in our cultural beliefs and serve more than 650 clients ranging from innovative biotech start-ups to established pharmaceutical companies. Our products, services and solutions help bring innovative therapies to market and support the patients who depend on them. Our jobs, skills and talents are unique, but together we make an impact every day. Join us! Across our growing organization, we embrace diversity in backgrounds and experiences. Improving patient lives around the world is a priority, and we need people from all backgrounds and swaths of life to help build the future of the healthcare and the life sciences industry. We believe our people make all the difference in cultivating an inclusive culture that embraces our cultural beliefs.&#xa0; We are deliberate and self-reflective about the kind of team and culture we are building. We look for team members that are not only strong in their own aptitudes but also who care deeply about EVERSANA, our people, clients and most importantly, the patients we serve. We are EVERSANA. 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.&#xa0; 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. 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 &#xa0; 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 OUR CULTURAL BELIEFS: </