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Senior AI Developer and Data Science Ph.D. Candidate with 6+ years of experience building and shipping production AI systems, including LLM agentic workflows (Agentic RAG, tool use, structured outputs) and automated evaluation harnesses. Focused on reliability, safety regression monitoring, and traceability across the full ML/LLM lifecycle. Experienced in AWS-based deployment architectures, data ingestion and feature pipelines, and RLHF-style feedback loops with human-in-the-loop review and audit logging. Proven impact improving accuracy, reducing hallucinations/execution failures, and lowering latency in high-concurrency systems.…Senior AI Developer and Data Science Ph.D. Candidate with 6+ years of experience building and shipping production AI systems, including LLM agentic workflows (Agentic RAG, tool use, structured outputs) and automated evaluation harnesses. Focused on reliability, safety regression monitoring, and traceability across the full ML/LLM lifecycle. Experienced in AWS-based deployment architectures, data ingestion and feature pipelines, and RLHF-style feedback loops with human-in-the-loop review and audit logging. Proven impact improving accuracy, reducing hallucinations/execution failures, and lowering latency in high-concurrency systems.

Akinkunle Akinola

AI Engineer, Data Scientist, Software Engineer, +2

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Senior AI Developer and Data Science Ph.D. Candidate with 6+ years of experience building and shipping production AI systems, including LLM agentic workflows (Agentic RAG, tool use, structured outputs) and automated evaluation harnesses. Focused on reliability, safety regression monitoring, and traceability across the full ML/LLM lifecycle. Experienced in AWS-based deployment architectures, data ingestion and feature pipelines, and RLHF-style feedback loops with human-in-the-loop review and audit logging. Proven impact improving accuracy, reducing hallucinations/execution failures, and lowering latency in high-concurrency systems.…Senior AI Developer and Data Science Ph.D. Candidate with 6+ years of experience building and shipping production AI systems, including LLM agentic workflows (Agentic RAG, tool use, structured outputs) and automated evaluation harnesses. Focused on reliability, safety regression monitoring, and traceability across the full ML/LLM lifecycle. Experienced in AWS-based deployment architectures, data ingestion and feature pipelines, and RLHF-style feedback loops with human-in-the-loop review and audit logging. Proven impact improving accuracy, reducing hallucinations/execution failures, and lowering latency in high-concurrency systems.

Available to hire

Senior AI Developer and Data Science Ph.D. Candidate with 6+ years of experience building and shipping production AI systems, including LLM agentic workflows (Agentic RAG, tool use, structured outputs) and automated evaluation harnesses. Focused on reliability, safety regression monitoring, and traceability across the full ML/LLM lifecycle.

Experienced in AWS-based deployment architectures, data ingestion and feature pipelines, and RLHF-style feedback loops with human-in-the-loop review and audit logging. Proven impact improving accuracy, reducing hallucinations/execution failures, and lowering latency in high-concurrency systems.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Language

English

Advanced

Work Experience

Senior Associate, AI/ML Engineer III at JPMorgan Chase

November 1, 2021 - Present

Designed, built, and shipped production AI services (batch and real-time) and LLM application workflows (Agentic RAG, tool calling, structured outputs) on AWS (Bedrock, ECS) using PyTorch, FastAPI, and LangGraph. Architected automated evaluation harnesses (RAGAS, LangSmith) and offline metrics pipelines embedded in CI/CD release gates to monitor prompt quality, model reliability, and safety regressions prior to production deployment. Built data ingestion and feature pipelines to connect training data with live model behavior, supporting ongoing feedback and RLHF-style alignment workflows. Supported NLP tasks including classification, summarization, information extraction, and content/compliance moderation for high-throughput enterprise applications. Integrated inference APIs with enterprise infrastructure, establishing human-in-the-loop review mechanisms and audit logging. Improved response accuracy by 25%, reduced execution failures/hallucinations by 40%, and cut p95 inference latency

Assistant Teaching Professor at Bowling Green State University

August 1, 2016 - December 1, 2021

Led quantitative research and teaching in statistical modeling and data quality validation. Documented reproducible experiments and methodologies for research and instruction.