AI Researcher Engineer - II
CodeRound is hiring for this role.
- AI Research Engineer
- Applied AI
- Machine Learning
- Python
- SQL
- LLM
- RAG
- Retrieval-Augmented Generation
- Embeddings
- Vector Databases
- Semantic Search
- Information Retrieval
- LLM Evaluation
- AI Systems
- Production ML
- Experimentation
- Model Evaluation
- Healthcare AI
We’re hiring an AI Research Engineer with 3–5 years of experience to bridge applied research and production AI systems. The role involves researching, designing, building, and optimizing Python-based AI solutions using LLMs, RAG, embeddings, and retrieval systems, working with complex longitudinal health data. We’re looking for someone who can independently drive experiments, establish evaluation frameworks, and ship reliable, scalable AI systems with measurable real-world impact.
What you'll do
- Review relevant research literature and identify techniques applicable to real-world AI problems.
- Formulate hypotheses, design experiments, and validate research findings against proprietary datasets.
- Build, deploy, and maintain production-quality AI systems using Python.
- Design and optimize embedding-based retrieval pipelines, including vector search, chunking, indexing, and ranking.
- Develop robust RAG and LLM-based systems with effective context construction and structured outputs.
- Create evaluation harnesses, benchmarks, and testing workflows to measure system quality and reliability.
- Define success metrics and baselines before experimentation, then conduct error analysis and iterate on results.
- Improve AI system accuracy, reliability, latency, and scalability through systematic experimentation.
- Own projects end-to-end, from research and prototyping through deployment, monitoring, and continuous improvement.
Must have
3–5 years of experience in applied AI research, ML engineering, or production AI development. Strong Python programming skills and solid SQL fundamentals. Hands-on experience building and deploying production-grade AI/ML systems. Strong understanding of embeddings, semantic search, vector databases, and information retrieval.Experience building and optimizing RAG pipelines, including chunking, indexing, retrieval, and context construction. Practical experience developing LLM-powered applications with structured outputs and prompt engineering. Experience designing evaluation frameworks, benchmarks, and systematic testing pipelines for AI systems. Ability to independently design experiments, analyze results, and translate research findings into production-ready solutions. Strong problem-solving skills and experience taking ownership of technical projects end-to-end.
Good to have
Research experience involving scientific literature reviews, hypothesis formulation, and experimental validation. Experience working with healthcare data, longitudinal datasets, or other complex structured and unstructured data.Familiarity with advanced retrieval techniques, reranking, hybrid search, and retrieval optimization. Experience with LLM evaluation, error analysis, observability, and production monitoring. Knowledge of model serving, inference optimization, latency reduction, and scalable AI architectures. Experience with cloud platforms, containerization, and production deployment workflows. A research-oriented academic background, publications, or contributions to relevant open-source projects.