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Niche LLM/production ML requirements and lesser-known employer reduce applicant competition.
Strong ML/LLM and production engineering focus limits transferability across industries.
Specific production ML, backend, and LLM skills increase screening rigor despite no explicit years.
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Build and deploy end-to-end AI-powered applications with Python and backend frameworks (FastAPI/Flask), handling full lifecycle from data ingestion to frontend integration.
Design and implement LLM-based solutions including RAG pipelines, prompt engineering, and evaluation workflows, ensuring production readiness with logging, monitoring, and performance optimization.
Develop scalable APIs and microservices for AI/ML functionalities and create reusable components and internal tools to improve development efficiency.
Strong proficiency in Python with hands-on backend development experience.
Experience in REST APIs, microservices architecture, modern web technologies (JavaScript, HTML, CSS), and ML/data processing libraries (Pandas, NumPy, Scikit-learn, or PyTorch).
Knowledge of LLMs, embeddings, vector databases, data pipelines, Git, Docker, CI/CD, and exposure to cloud platforms (AWS/Azure/GCP).
Bachelor’s or master’s degree in computer science, engineering, or related discipline. Work Experience Required: Not explicitly mentioned in the JD.
Experienced in building and deploying production AI applications with a strong backend and ML integration focus, including LLM-based solutions.
Familiar with microservices architecture, scalable API design, and modern AI architectures with practical knowledge of vector databases and AI tooling.
Comfortable working across the full AI application lifecycle including statistical model validation, monitoring, and enhancing AI development productivity using AI-assisted tools.