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Niche LLM/vector DB specialization and lesser-known employer reduce applicant competition.
ML/LLM engineering skills are broadly transferable across industries despite optional domain experience preferences.
Multiple mandatory years, AI experience, and specific LLM/vector/Kubernetes skills enforce strict filters.
Design, develop, and maintain production-grade AI-powered applications leveraging LLMs, generative AI, vector databases, and agent frameworks including Retrieval-Augmented Generation (RAG) systems.
Architect scalable, secure, cloud-native software systems, develop APIs and microservices with a strong emphasis on performance, reliability, observability, and security standards.
Lead AI model management including evaluation, benchmarking, optimization, monitoring, and collaborate across teams to manage AI infrastructure and deployment pipelines.
Bachelor's degree in Computer Science, Software Engineering, or related field, or equivalent practical experience.
5+ years of professional software engineering experience with at least 2 years in AI, machine learning, or generative AI application development.
Proficiency in modern programming languages such as Python, TypeScript, Java, or C#; experience deploying cloud-native applications on AWS, Azure, or Google Cloud.
Hands-on experience with LLMs and AI platforms (e.g., OpenAI, Anthropic, Azure OpenAI, Google Gemini), implementing RAG solutions, vector databases, microservices, RESTful APIs, and familiarity with software development lifecycle, CI/CD, and DevOps.
Experienced in end-to-end AI application lifecycle including prompt engineering, AI quality evaluation, and deployment of agentic AI frameworks (e.g., LangChain, Semantic Kernel).
Skilled in designing and leading complex AI software architectures with a focus on scalability, observability, and security in cloud environments.
Experienced collaborator able to lead technical discussions, mentor peers, and communicate AI concepts to both technical and non-technical stakeholders.