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Popular ML role with broad required skills, but smaller firm and non-metro location reduce applicant density.
Medium — core ML skills are transferable, but LLM/RAG and customer-facing specialization reduce portability.
High — explicit 5–8 years plus mandatory LLM, MLOps, and customer-facing experience required.
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Lead end-to-end AI project lifecycle including use case scoping, model development, deployment, and optimization focused on Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG).
Design and implement scalable, data-centric AI systems with expertise in vector databases, text processing pipelines, and Gen AI system architecture using orchestration frameworks such as LangChain and LlamaIndex.
Manage production deployment of AI models including LLMOps best practices, CI/CD, observability, and act as primary technical liaison with business stakeholders for AI adoption and enablement.
5+ years of customer-facing experience designing and implementing AI/ML solutions.
Proficient in Python and SQL with experience in AI frameworks like scikit-learn, Hugging Face Transformers, LangChain, and cloud platforms such as GCP.
Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or equivalent quantitative field.
Excellent English communication skills including presentation to technical and executive audiences.
Experienced in strategic customer engagement including translating business needs into technical AI solutions and conducting stakeholder education and workshops.
Technically strong in building advanced Generative AI systems with expertise across retrieval, augmentation, fine-tuning, and evaluation of models.
Able to manage multiple fast-paced projects simultaneously, balancing priorities while maintaining deep technical ownership of AI deployments.