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Protocol Intelligence
Data-driven signals on your job's competitivenessMid-level, popular GenAI role with common LLM skills increases candidate density.
Specialized GenAI/LLM and LLMOps skills reduce cross-industry transferability.
Explicit 4-5 years plus mandatory LLM, cloud, IaC, and vector DB skills raise shortlist strictness.
Job Description
Structured overview of role & requirementsAbout This Role
Design, develop, and operate secure, scalable, and trustworthy Generative AI applications and reusable platform components aligned with the company's AI Strategy and Governance.
Implement advanced LLM-powered solutions including Prompt Engineering, Context Architecture, and Retrieval-Augmented Generation (RAG) with build-test-deploy pipelines to ensure quality and reproducibility.
Take ownership of GenAI platform modules, monitor system and model performance, troubleshoot issues, and collaborate cross-functionally to deliver AI-driven automation and reusable components.
Minimum Requirements
4-5 years of professional experience as an application developer, preferably in AI/ML or backend service development.
Strong hands-on experience with large language models (Azure OpenAI, Anthropic, Gemini) and AI development techniques (Prompt Engineering, Context Management, RAG).
Proficiency in Python, SQL, JavaScript/TypeScript; experience with cloud resource management using Infrastructure as Code (Terraform) on GCP/Azure.
Bachelor’s degree in computer science, engineering, or related field; English language proficiency; certifications like GitHub Certified Agentic AI Developer or Claude Certified Architect - Foundations considered advantageous.
Ideal Candidate Profile
Experienced in designing and deploying enterprise-grade AI applications emphasizing security, scalability, explainability, and AI safety mechanisms.
Practiced in translating complex business requirements into production-ready, user-friendly AI solutions within cross-functional teams.
Comfortable working with LLMOps/AgentOps tooling, orchestration engines, and vector databases, demonstrating a hands-on approach to innovation and continuous improvement.