Sr AI Engineer (Generative AI & Pharmacovigilance)

Jobgether; India; Hybrid; INR 3,000,000 - 5,400,000; Full time

Job summary

Jobgether is seeking a Sr AI Engineer (Generative AI & Pharmacovigilance) in India. You will design and deploy production-grade AI solutions spanning generative AI, LLMs, RAG, NLP, and agentic AI.

Your work will support adverse event processing, signal detection, regulatory reporting, and literature surveillance, in a highly regulated life sciences environment. You will collaborate with pharmacovigilance experts, data scientists, safety operations teams, and software engineers to translate

Qualifications

• 5+ years of hands-on experience in AI/ML engineering and deploying production-grade AI applications.
• Mandatory experience with Agentic AI frameworks and GenAI/LMM/NLP.
• Strong Python skills; experience with SQL and REST APIs.
• Experience with transformer models, NLP, and model fine-tuning.
• Exposure to Azure OpenAI, OpenAI APIs, LangChain, vector databases, and RAG architectures.

Responsibilities

• Design, develop, and deploy AI/ML solutions for pharmacovigilance and drug safety processes.
• Build Generative AI applications using OpenAI, Azure OpenAI, Anthropic, Llama, etc.
• Develop domain-specific AI assistants and workflows for pharmacovigilance operations.
• Create document-processing solutions for ICSRs, safety narratives, and regulatory submissions.
• Architect retrieval-augmented generation (RAG) with vector databases and semantic search.
• Develop prompt-engineering frameworks and model evaluation methods.
• Implement AI agents and workflow automation using Agentic AI frameworks.
• Build data pipelines integrating structured and unstructured data for pharmacovigilance; ensure secure, compliant deployments.

Skills

AI/ML engineering

Agentic AI

Generative AI

Python

REST APIs

SQL

MLOps

Kubernetes

NLP

Regulatory knowledge

Education

Bachelor's in CS

Master's preferred

Tools

Azure OpenAI

OpenAI APIs

LangChain

LlamaIndex

Graph databases

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Sr AI Engineer (Generative AI & Pharmacovigilance) based in India.

This is a senior AI engineering opportunity focused on building next-generation technology for pharmacovigilance and drug safety. You will design, develop, and deploy production-grade AI solutions using Generative AI, LLMs, RAG, NLP, Machine Learning, and Agentic AI. Your work will support critical processes such as adverse event case processing, signal detection, literature surveillance, regulatory reporting, and medical document intelligence. You will collaborate closely with pharmacovigilance experts, product leaders, data scientists, safety operations teams, and software engineers. The role combines hands-on engineering with innovation, requiring you to turn complex healthcare and regulatory requirements into scalable AI products. You will also help establish responsible, observable, secure, and compliant AI practices within a highly regulated life sciences environment.

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.