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Protocol Intelligence
Data-driven signals on your job's competitivenessMid-level (4-7 yrs) role in metro locations with moderate employer brand and niche LLM specialization.
Specialized LLM and agent framework skills reduce transferability but remain moderately transferable across industries.
Explicit 4-7 years requirement plus mandatory LLM/agent frameworks, MLOps, and AWS stack increases screening rigor.
Job Description
Structured overview of role & requirementsAbout This Role
Architect and deploy scalable machine learning solutions including LLM-powered conversational AI and agent frameworks on AWS cloud.
Lead end-to-end ML lifecycle: data prep, feature engineering, model development, validation, deployment, and monitoring.
Design production-grade agent frameworks, tool calling systems, and multi-agent coordination, applying MLOps best practices.
Minimum Requirements
4-7 years of professional experience in machine learning engineering.
Strong expertise in traditional ML (regression, decision trees, SVM, ensemble models) and LLM/conversational AI systems.
Proficiency with Python ML libraries (scikit-learn, XGBoost, LightGBM) and agent frameworks (LangChain, CrewAI).
Experience working with AWS ML services such as SageMaker and Bedrock.
Ideal Candidate Profile
Experience building enterprise-grade conversational AI systems and scalable ML/AI solutions in cross-functional teams.
Hands-on with prompt engineering, RAG systems, semantic search, and advanced agent reasoning patterns.
Proven ability to deliver production-ready APIs and conversation analytics tools integrating ML models with enterprise applications.