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Popular mid-level AI/ML role with broad LLM/RAG requirements increases candidate competition.
Core ML and MLOps skills are transferable across industries, though applied domain knowledge varies.
Explicit 2+ years requirement plus many mandatory ML, MLOps, and cloud skills.
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Lead end-to-end AI projects focused on Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG), from use case scoping to deployment and optimization.
Design and implement scalable AI solutions including text processing pipelines, vector databases, and generative AI architectures using frameworks like LangChain and LlamaIndex.
Manage production deployment and operations of AI models with best practices in LLMOps, CI/CD, monitoring, and conduct stakeholder engagement and knowledge transfer workshops.
Minimum 2 years experience in data science with Python, SQL, and data engineering skills.
Bachelor’s degree in Computer Science, Computer Engineering, or related field, or equivalent work experience.
Experience with Generative AI (LLMs, prompt engineering, RAG) and MLOps including CI/CD, Docker, API development, model versioning.
Experience with Google Cloud tools (Vertex AI, BigQuery, Cloud Run, etc.) and ability to communicate technical processes to non-technical stakeholders.
Experienced in architecting and deploying advanced generative AI systems integrating multiple components of the Gen AI stack.
Demonstrated ability to work directly with business stakeholders to translate requirements and enable adoption of AI solutions.
Comfortable managing end-to-end model lifecycle including data curation, fine-tuning, deployment, and performance evaluation in production environments.