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Specialized LLM/vector DB skillset reduces applicants though metro location and mid-level ML role attract moderate competition.
ML/AI engineering skills are transferable across industries but specific LLM and integration expertise increase domain bias.
Multiple mandatory technical skills and production deployment experience make filtering moderately strict.
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Identify and define AI use cases with business stakeholders and deliver production-grade AI/ML and GenAI solutions, including applications and automation tools.
Design, build, and deploy prototypes and scalable AI systems integrating with enterprise APIs, databases, and cloud platforms (AWS/Azure/GCP).
Monitor, optimize, and document AI solutions focusing on performance metrics like accuracy, latency, cost, and advise stakeholders on AI capabilities and risks.
Bachelor’s degree in Computer Science, Engineering, Data Science or equivalent.
Strong programming skills in Python and JavaScript/TypeScript or Java.
Hands-on experience with AI/ML technologies including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), embeddings, and vector databases.
Experience with API development, cloud platforms (AWS, Azure, or GCP), and enterprise system integration.
Experienced in operating at the intersection of AI technology and business needs, capable of translating stakeholder requirements into technical solutions.
Skilled in end-to-end system design and deployment of AI-powered applications in fast-paced, customer-facing environments.
Comfortable working on both prototyping and production deployment phases with emphasis on scalability, security, and performance optimization.