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Hi! I’m Jared Hoffen, a Senior AI Engineer with 12+ years of hands-on experience building production-grade backend systems, machine learning pipelines, and GenAI applications. I specialize in LLM agents, retrieval-augmented generation (RAG), and distributed AI systems, with a proven track record of delivering scalable, low-latency, cost-efficient AI solutions, along with strong evaluation, reliability, and production monitoring. I’m passionate about aligning AI with real-world workflows, designing robust guardrails and observability, and collaborating across data, ML, and platform teams to ship impactful AI at scale.…Hi! I’m Jared Hoffen, a Senior AI Engineer with 12+ years of hands-on experience building production-grade backend systems, machine learning pipelines, and GenAI applications. I specialize in LLM agents, retrieval-augmented generation (RAG), and distributed AI systems, with a proven track record of delivering scalable, low-latency, cost-efficient AI solutions, along with strong evaluation, reliability, and production monitoring. I’m passionate about aligning AI with real-world workflows, designing robust guardrails and observability, and collaborating across data, ML, and platform teams to ship impactful AI at scale.

Jared Hoffen

AI Engineer, Data Scientist, Back-End Developer, +2

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Hi! I’m Jared Hoffen, a Senior AI Engineer with 12+ years of hands-on experience building production-grade backend systems, machine learning pipelines, and GenAI applications. I specialize in LLM agents, retrieval-augmented generation (RAG), and distributed AI systems, with a proven track record of delivering scalable, low-latency, cost-efficient AI solutions, along with strong evaluation, reliability, and production monitoring. I’m passionate about aligning AI with real-world workflows, designing robust guardrails and observability, and collaborating across data, ML, and platform teams to ship impactful AI at scale.…Hi! I’m Jared Hoffen, a Senior AI Engineer with 12+ years of hands-on experience building production-grade backend systems, machine learning pipelines, and GenAI applications. I specialize in LLM agents, retrieval-augmented generation (RAG), and distributed AI systems, with a proven track record of delivering scalable, low-latency, cost-efficient AI solutions, along with strong evaluation, reliability, and production monitoring. I’m passionate about aligning AI with real-world workflows, designing robust guardrails and observability, and collaborating across data, ML, and platform teams to ship impactful AI at scale.

Available to hire

Hi! I’m Jared Hoffen, a Senior AI Engineer with 12+ years of hands-on experience building production-grade backend systems, machine learning pipelines, and GenAI applications. I specialize in LLM agents, retrieval-augmented generation (RAG), and distributed AI systems, with a proven track record of delivering scalable, low-latency, cost-efficient AI solutions, along with strong evaluation, reliability, and production monitoring.

I’m passionate about aligning AI with real-world workflows, designing robust guardrails and observability, and collaborating across data, ML, and platform teams to ship impactful AI at scale.

Skills

Experience Level

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Work Experience

Senior AI Engineer — Gen AI Systems & Agent Platform at Anthropic (Contract Engagement)

January 1, 2024 - Present

Designed and deployed LLM agent systems supporting multi-step reasoning and tool execution across production workflows. Built multi-agent orchestration systems with memory and dynamic context routing, improving task success rate by ~20–30% via vector DB-backed RAG pipelines. Implemented Retriever-Augmented Generation (RAG) pipelines including chunking, embeddings, and hybrid retrieval for large-scale document systems. Developed evaluation frameworks (LLM-as-a-judge, scoring systems) reducing redaction detection time by ~40%. Optimized inference cost and latency via prompting engineering, caching, and batch ingest, reducing keystroke usage by ~25–35%. Designed AI safety rails including prompt injection mitigation and output validation systems. Built observability systems with prompt tracing and tool-call logging improving debugging and incident response efficiency.

Machine Learning Engineer — Applied AI Systems at Airbnb

January 1, 2019 - December 1, 2023

Built ranking and recommendation systems using Python-based ML pipelines for large-scale marketplace personalization. Developed NLP pipelines for processing large-scale text data for classification and tagging. Designed AB testing framework for model and product experiments. Built real-time and batch inference pipelines optimized for production latency. Developed feature engineering pipelines for structured and unstructured datasets. Improved ranking performance via iterative model tuning and feature optimization. Implemented monitoring for model drift, performance tracking, and regression detection. Used MongoDB to support flexible data models for ML feature metadata, experimentation workflows, and large-scale text/data processing pipelines.

Software Engineer II — Data & ML Infrastructure at HubSpot

July 1, 2016 - December 1, 2018

Built event-driven data pipelines using Python backend services for large-scale telemetry processing. Developed backend infrastructure supporting ML workflows and experimentation systems. Improved observability (logging and tracing), reducing debugging and incident resolution time. Increased pipeline reliability using retry logic, idempotency patterns, and backfill strategies. Optimized backend services for throughput, latency, and database performance. Built and optimized backend services using MongoDB for product telemetry, workflow metadata, indexing, and query performance.

Backend Engineer — Developer Platform at DigitalOcean

July 1, 2013 - May 1, 2016

Built scalable REST APIs following backend engineering practices in Python and distributed systems architecture. Designed and optimized PostgreSQL schemas for scalability and query performance. Implemented authentication, rate limiting, and caching using Redis. Improved system performance through backend optimization and refactoring. Supported AWS-based deployments and early infrastructure setup. LLM Knowledge Assistant (RAG System) and Genetic Workflow Automation System: built knowledge assistant with RAG, multi-step workflows.