Francis Ngigi
@francisngigi
Senior full-stack software engineer specializing in high-performance AI infrastructure and scalable production systems.
What I'm looking for
I am a senior full-stack software engineer with 5+ years building high-performance systems at AI startups and quantitative trading firms. I specialize in scalable APIs, distributed databases, and production systems that power large-scale ML and inference workloads.
At a Series B stealth AI startup I led the design and deployment of a distributed vector database supporting 500M+ embeddings with sub-10ms p99 latency and productionized 30+ REST and GraphQL APIs handling 50K+ QPS. I also drove a migration to partitioned and sharded PostgreSQL schemas that cut costs 68% while enabling 10× data growth.
Previously at a quantitative trading firm I developed high-throughput market-making systems in OCaml and Python, maintained 40TB+ time-series SQL schemas, and built internal APIs and trading bots. I am recognized for rigorous problem-solving, extreme attention to detail, and improving system reliability through strong review practices.
I mentor engineers, lead technical teams, and adopt productivity tooling such as coding agents to accelerate delivery. I am immediately available for high-impact sprints and open to contractor engagements with flexible payout terms.
Experience
Work history, roles, and key accomplishments
Senior Software Engineer
Stealth AI Startup
Jun 2022 - Present (3 years 6 months)
Led design and deployment of a distributed vector database supporting 500M+ embeddings with sub-10ms p99 latency and productionized 30+ REST and GraphQL APIs handling 50K+ QPS for real-time 70B-parameter model inference.
Developed high-throughput market-making systems in OCaml and Python processing 2M+ trades/sec with <50µs latency and built REST/gRPC services consumed by 200+ quantitative researchers, improving analytics performance 12×.
Education
Degrees, certifications, and relevant coursework
Stanford University
Bachelor of Science, Computer Science
2016 - 2020
Grade: 3.9/4.0
Activities and societies: Relevant coursework: Distributed Systems, Advanced Databases, Machine Learning Systems, Compilers.
Completed a Bachelor of Science in Computer Science with a 3.9 GPA, focusing on distributed systems, databases, and machine learning systems.
Tech stack
Software and tools used professionally
Availability
Location
Authorized to work in
Job categories
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