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AI Applied Scientist
We’re expanding our AI team and looking for an AI Applied Scientist to design, build, and deploy intelligent systems powered by large language models, AI agents, and retrieval-based architectures.
The role focuses on applying state-of-the-art LLMs in real-world products, including agentic workflows, tool calling, and Retrieval-Augmented Generation (RAG). You’ll work closely with engineering and product teams to turn research ideas into scalable, reliable AI solutions.
You’ll be involved in experimenting with models, architecting AI pipelines, evaluating performance, and continuously improving systems that interact with data, tools, and users in production environments.
Location: Hybrid
Company: Technoperia
Engagement: Full-time
Minimum Requirements
• 2–4 years of professional experience in applied machine learning, AI engineering, or a related role • Strong understanding of large language models (LLMs) and modern NLP techniques • Hands-on experience building AI agents and agentic workflows • Experience with tool calling and function/tool integration for LLMs • Practical experience designing and implementing RAG systems (vector databases, embeddings, retrieval pipelines) • Proficiency in Python and common ML/AI libraries (PyTorch, Hugging Face, LangChain/LlamaIndex or similar) • Solid understanding of data preprocessing, evaluation, and prompt engineering • Experience working with APIs and integrating AI systems into backend services • Ability to design scalable, maintainable AI pipelines for production use • Strong problem-solving skills and experimental mindset • Comfortable collaborating in cross-functional teams and explaining complex concepts clearly
Nice to Have
• Experience deploying AI/ML systems to production (cloud environments such as AWS, GCP, or Azure) • Familiarity with vector databases (Pinecone, Weaviate, FAISS, Qdrant, or similar) • Experience with fine-tuning or adapting LLMs • Knowledge of MLOps practices, monitoring, and model evaluation in production • Experience working with multimodal models (text, image, or audio) • Background in research, experimentation, or rapid prototyping of AI-driven features