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Protocol Intelligence
Data-driven signals on your job's competitivenessMid-level metro role at a known employer but very specialized LLM/RAG skills narrow candidate pool.
Specialized LLM, RAG, fine-tuning, and vector DB expertise makes cross-industry transfer difficult.
Many mandatory LLM, RAG, fine-tuning, vector DB and cloud/Kubernetes requirements increase filter strictness.
Job Description
Structured overview of role & requirementsAbout This Role
Lead end-to-end technical design and implementation of enterprise-scale AI solutions including production-ready applications using LLMs and hybrid RAG systems with vector and graph databases.
Perform architecture and code reviews, ensure solution quality, scalability, security, and compliance with AI ethics and regulatory standards (e.g., GDPR).
Provide technical leadership by mentoring development teams, resolving technical challenges, and guiding AI best practices implementation.
Minimum Requirements
5+ years Python development experience with strong software engineering fundamentals.
Bachelor's or Master's degree in Computer Science, AI/ML, or equivalent practical experience.
Hands-on experience with LLM APIs (OpenAI, Anthropic, Google) and frameworks like TensorFlow, PyTorch, Hugging Face.
Experience with vector databases, graph databases, cloud platforms (AWS, GCP, Azure), containerization (Docker, Kubernetes), and security/compliance protocols (data anonymization, bias detection).
Ideal Candidate Profile
Experienced in building complex AI systems integrating multiple models (text, vision, audio) with event-driven architectures and multi-agent workflows.
Proficient at designing and deploying hybrid RAG systems leveraging knowledge graphs and vector embeddings for multi-hop reasoning on operational data.
Skilled in AI model fine-tuning (LoRA/QLoRA), prompt engineering, memory management frameworks, and ensuring responsible AI deployment with security and compliance rigor.