Applied AI Engineer
Skills
About the role
This is a hybrid Applied AI Engineer role in Bengaluru for an engineer building document-processing, retrieval and agentic systems. You will create production LLM applications for pharmaceutical and life-sciences workflows involving regulatory documentation and technical authoring. The role suits an engineer who can improve AI accuracy, source attribution and reliability in a regulated enterprise setting.
What you’ll do
- Build processing pipelines for structured and unstructured documents.
- Design and improve retrieval and RAG systems for precise search.
- Develop AI agents and LLM-based content-generation workflows.
- Create verification and evaluation systems for generated outputs.
- Build conversational AI with reliable source attribution.
- Improve AI workloads for reliability, speed and cost.
- Develop scalable infrastructure for production AI applications.
- Work with product and engineering teams on customer-facing features.
What they’re looking for
- Experience with AI engineering, machine learning and generative AI.
- Knowledge of LLMs, RAG, AI agents and agentic workflows.
- Experience with NLP, OCR, vector databases and semantic search.
- Python development and prompt or context engineering skills.
- Understanding of information retrieval and enterprise AI.
- Ability to build production systems for B2B SaaS products.
What’s on offer
- Full-time hybrid work in Bengaluru.
- Compensation of ₹25 lakh to ₹30 lakh per year.
- The client is a VC-backed HealthTech startup with $720,000 raised.
- The product automates regulatory documentation, technical authoring and compliance workflows for pharmaceutical and life-sciences organizations.
Questions about this role
Is this Applied AI Engineer role hybrid?
Yes. It is a hybrid role in Bengaluru.
What is the salary range?
The listed compensation is ₹25 lakh to ₹30 lakh per year.
How much experience is requested?
The posting asks for 2+ years of experience.
What kind of documents will the role handle?
The role handles structured and unstructured documents used in regulatory and technical workflows.
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