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Company Description
BootsRad Technologies is a dynamic and innovative tech company committed to advancing cutting-edge solutions in artificial intelligence and machine learning. Based in the vibrant technology hub of Noida, the company focuses on developing impactful, scalable, and future-ready products and services. At BootsRad, we prioritize creativity, collaboration, and technical excellence to solve complex challenges and empower businesses across various industries with AI-driven solutions.
Senior AI/ML Engineer (2 Openings)
## Role Overview
You’ll be one of the core architects shaping our AI platform — designing LLM pipelines, building scalable RAG systems, and enabling domain experts to convert knowledge into deployable AI agents.
## Key Responsibilities
• Design and deploy *production-grade LLM systems* (prompting, fine-tuning, evaluation)
• Architect and optimize *RAG pipelines* (vector DBs, embeddings, retrieval strategies)
• Build *multi-agent / agentic workflows* for complex task execution
• Develop *scalable ML pipelines* (training, inference, monitoring)
• Integrate *Vision-Language Models (VLMs)* and basic CV modules where required
• Lead *system design decisions* for AI infrastructure and APIs
• Mentor mid-level engineers and interns
## Required Skills
• 4–8+ years in AI/ML engineering
• Strong experience with:
* Python, PyTorch / TensorFlow
* LLM frameworks (LangChain, LlamaIndex, Haystack, etc.)
* Vector databases (FAISS, Pinecone, Weaviate, etc.)
• Deep understanding of:
* RAG architectures
* Prompt engineering & LLM evaluation
* Embeddings & semantic search
• Experience with *cloud (AWS/GCP/Azure)* and scalable deployments
• Basic experience in:
* Computer Vision (OpenCV, detection models, etc.)
* VLMs (CLIP, GPT-4V-type systems, etc.)
## Good to Have
• Experience with *agentic AI frameworks*
• Knowledge of *geospatial / infra / CAD systems* (big plus for your domain)
• Experience building *developer platforms / APIs*
## What We’re Looking For
• Builder mindset > academic perfection
• Can own systems end-to-end
• Comfortable with ambiguity and rapid iteration
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