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Protocol Intelligence
Data-driven signals on your job's competitivenessTier-1 brand, popular AI Engineer title, metro location, and broad skillset increase candidate competition.
Core ML/LLM engineering skills are broadly transferable across industries, so industry bias is low.
Requires production ML deployments, cloud/LLMOps experience and Responsible AI, so technical filters moderate.
Job Description
Structured overview of role & requirementsAbout This Role
Design, develop, and implement AI and Generative AI solutions using LLMs, Agentic AI frameworks, RAG architectures, and cloud AI platforms.
Develop and maintain scalable AI/ML pipelines, data preparation workflows, and cloud-native integrations with platforms like Azure, Databricks, and AWS.
Implement Responsible AI governance practices including bias detection, hallucination mitigation, explainability, and compliance with data ethics standards.
Minimum Requirements
Bachelor’s degree in Computer Science, Data Science, AI/ML, or related technical field.
Hands-on experience in developing and deploying production-level AI applications.
Experience with Python, Spark or SQL, and exposure to at least one cloud platform (Azure, AWS, Databricks) and containerization (Docker or Kubernetes).
Work Experience Required: Not explicitly mentioned in the JD.
Ideal Candidate Profile
Experienced in cloud-native AI/ML solution development and operational maintenance following MLOps and LLMOps best practices.
Knowledgeable in AI ethics and governance with ability to implement Responsible AI frameworks.
Familiar with AI/ML workflows, LLMs, generative AI concepts, software development lifecycle, and version control tools like Git.