Open to opportunities

Lindsay jackson

@lindsayjackson4

Message

Senior AI systems engineer delivering scalable, fault-tolerant LLM/RAG platforms with measurable impact.

United States

Message

What I'm looking for

I’m looking for a team where I can build and ship production LLM/RAG systems—tight latency and cost targets, strong observability, and CI/CD ownership—collaborating closely with ML and domain experts to deliver reliable, scalable outcomes.

I’m a Senior AI/ML systems engineer with more than 10 years building scalable, fault-tolerant AI platforms across startup and enterprise environments. I specialize in RAG architectures, vector databases, and production-grade AI agent orchestration, and I’ve worked closely with platform, ML, and domain experts to ship solutions that actually move performance and reliability.

I’ve led production deployments that reduced inference latency by 40%, cut costs via batching and caching, and sustained 99.9% service reliability. From FastAPI model-serving microservices and Kubernetes agent clusters to observability stacks and CI/CD automation, I focus on improving latency, throughput, and system resilience while keeping pipelines and operations dependable.

Experience

Work history, roles, and key accomplishments

Current

Lead AI Systems Engineer

Current

Hexagon

Jan 2025 - Present (1 year 5 months)

Architected RAG pipelines with LangChain, OpenAI, and Pinecone, reducing average query latency by 42%. Orchestrated and served production LLM agents on Kubernetes, achieving 99.95% availability and improving median API response times by 35%.

RAG Lang Chain OpenAI Pinecone Kubernetes fastAPI Milvus Pgvector Terraform

AI Systems Engineer

Epoq Legal US

Sep 2023 - Jan 2025 (1 year 4 months)

Troubleshot LLM integration issues with tracing across FastAPI services, reducing diagnostic time by 60%. Deployed production RAG solutions using ChromaDB and Pgvector, enabling 500k document lookups per day and improving retrieval precision by 21%.

fastAPI LLM Integrations RAG ChromaDB Pgvector AutoGen GitHub Actions

Platform Software Engineer

Oracle

Jun 2020 - Sep 2023 (3 years 3 months)

Modernized backend systems with Helm and standardized Kubernetes deployments, reducing environment drift by 90%. Migrated monolithic search to microservices with PostgreSQL and Elasticsearch, improving throughput by 3x and reducing latency by 40%, while reaching 99.9% availability via chaos testing and SLO-based alerts.

Kubernetes Helm Microservices gRPC PostgreSQL Elasticsearch Pgvector OpenTelemetry Chaos Engineering

Full Stack Engineer

The Home Spot

Jan 2017 - Jun 2020 (3 years 5 months)

Evaluated search architectures and implemented Elasticsearch indices, improving search relevance by 45%. Standardized backend APIs with Node.js/Express and built ingestion/ETL workflows, enabling partner integrations and improving onboarding speed by 2x.

Elasticsearch Node Express ETL PostgreSQL Python SpaCy Semantic Search

Software Development Engineer

Amazon

Mar 2015 - Jan 2017 (1 year 10 months)

Rewrote internal service components in Java to increase throughput and capacity by 25% under load tests. Improved reliability and delivery by consolidating monitoring (reducing alert noise by 33%), adopting infrastructure as code with CloudFormation (reducing provisioning time by 70%), and implementing API versioning to cut regressions by 40%.

Java REST APIs Cloudwatch CloudFormation Infrastructure as Code Redis Performance Tuning Postmortems