Sr AI Engineer (Generative AI & Pharmacovigilance)
New
J
JobgetherLife Sciences
IndiaFull-TimeSenior
Salary not disclosed
Apply NowOpens the employer's application page
Job Details
- Experience
- 5+ years
- Required Skills
- AWSPostgreSQLPythonSQLAzureMLOpsGenerative AI
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.
- Practical experience with MLOps and production infrastructure, including MLflow, Docker, Kubernetes, CI/CD pipelines, and AI observability.
- Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Engineering, Physics, Bioinformatics, or a related field.
Responsibilities
- 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.
- Deploy AI models into production while implementing monitoring, drift detection, performance optimization, and observability.
View Full Description & ApplyYou'll be redirected to the employer's site
