Job VC
Middle AI R&D Engineer (Prototypes & Innovation) 68176
Technologies
Description
Indeema Software is looking for a skilled AI R&D Engineer to join our Team.
Requirements:
2+ years of experience in software engineering, ML engineering, or applied AI development;
Strong understanding of AI/ML fundamentals, especially transformer-based architectures;
Hands-on experience with LLMs, including prompt engineering, RAG systems, and/or fine-tuning;
Strong Python proficiency and ability to build production-quality prototypes (PyTorch, FastAPI or similar);
Experience with building AI workflows using frameworks such as LangChain, LlamaIndex or equivalent;
Understanding of ML concepts: loss functions, optimization, evaluation metrics, overfitting;
Experience working with embeddings, vector databases, and retrieval pipelines;
Ability to design and run experiments, evaluate results rigorously, and document findings clearly;
Comfortable reading and interpreting research papers and applying relevant ideas in practice;
Experience with Git and collaborative development workflows (code reviews, CI/CD basics, testing practices);
Strong analytical thinking and ability to work independently in ambiguous environments;
Good written communication skills (documentation of experiments, technical decisions, results).
Nice to Have:
Experience with model optimization (quantization, ONNX, TensorRT).
Cloud deployment experience (AWS / GCP / Azure);
Basic frontend tools for demos (Streamlit, Gradio, React);
Experience with NVIDIA Jetson or edge AI devices;
Familiarity with CUDA, JetPack, DeepStream;
Kaggle, hackathons, or open-source contributions;
Domain expertise (healthcare, finance, legal, etc.).
Responsibilities:
Research and evaluate new AI models, techniques, and architectures together with the R&D team;
Design, run, and analyze experiments, ensuring reproducibility and clear documentation of results;
Rapidly develop AI prototypes (agents, RAG systems, classifiers, copilots, vision-based solutions);
Benchmark different approaches and present results to stakeholders to support product decisions;
Transform successful prototypes into production-ready features in collaboration with engineering teams;
Deploy and test AI models, including on edge devices such as NVIDIA Jetson;
Read and critically evaluate research papers and extract actionable insights;
Contribute to the technical roadmap with ideas based on research and experimentation;
Promote strong engineering and research practices (clean code, reproducibility, experiment rigor);
Share knowledge with the team through demos, documentation, and internal presentations.
Requirements:
2+ years of experience in software engineering, ML engineering, or applied AI development;
Strong understanding of AI/ML fundamentals, especially transformer-based architectures;
Hands-on experience with LLMs, including prompt engineering, RAG systems, and/or fine-tuning;
Strong Python proficiency and ability to build production-quality prototypes (PyTorch, FastAPI or similar);
Experience with building AI workflows using frameworks such as LangChain, LlamaIndex or equivalent;
Understanding of ML concepts: loss functions, optimization, evaluation metrics, overfitting;
Experience working with embeddings, vector databases, and retrieval pipelines;
Ability to design and run experiments, evaluate results rigorously, and document findings clearly;
Comfortable reading and interpreting research papers and applying relevant ideas in practice;
Experience with Git and collaborative development workflows (code reviews, CI/CD basics, testing practices);
Strong analytical thinking and ability to work independently in ambiguous environments;
Good written communication skills (documentation of experiments, technical decisions, results).
Nice to Have:
Experience with model optimization (quantization, ONNX, TensorRT).
Cloud deployment experience (AWS / GCP / Azure);
Basic frontend tools for demos (Streamlit, Gradio, React);
Experience with NVIDIA Jetson or edge AI devices;
Familiarity with CUDA, JetPack, DeepStream;
Kaggle, hackathons, or open-source contributions;
Domain expertise (healthcare, finance, legal, etc.).
Responsibilities:
Research and evaluate new AI models, techniques, and architectures together with the R&D team;
Design, run, and analyze experiments, ensuring reproducibility and clear documentation of results;
Rapidly develop AI prototypes (agents, RAG systems, classifiers, copilots, vision-based solutions);
Benchmark different approaches and present results to stakeholders to support product decisions;
Transform successful prototypes into production-ready features in collaboration with engineering teams;
Deploy and test AI models, including on edge devices such as NVIDIA Jetson;
Read and critically evaluate research papers and extract actionable insights;
Contribute to the technical roadmap with ideas based on research and experimentation;
Promote strong engineering and research practices (clean code, reproducibility, experiment rigor);
Share knowledge with the team through demos, documentation, and internal presentations.