Senior AI Software Engineer

Harnham; San Francisco (CA); On-site; USD 180,000 - 260,000; Full time

Job summary

Harnham is partnering with a high-growth, mission-driven SaaS company to redefine enterprise trust with AI at the core. You will architect AI systems, shape LLM pipelines, and drive evaluation and cost efficiency across production deployments.

You will collaborate across product, platform, and domain teams to translate complex requirements into scalable architectures and systems that emphasize reliability and auditability.

Qualifications

• 6+ years of software engineering with 3+ years in ML/AI systems.
• Proven experience deploying production LLM systems.
• Strong Python with solid CS fundamentals.

Responsibilities

• Design and own production AI systems end-to-end including LLM pipelines and orchestration layers.
• Build and scale RAG systems, embedding pipelines, and vector-based search infrastructure.
• Define evaluation frameworks for retrieval quality and system performance.
• Analyze production behavior, identify failure modes, and drive data-driven improvements.
• Make architectural decisions impacting team direction and tooling.
• Collaborate with product, platform, and domain teams to translate requirements into scalable systems.
• Lead best practices for reliable, observable, and cost-efficient AI systems.

Skills

6+ years experience

3+ years ML/AI

Production LLM systems

RAG embeddings

Vector databases

Python

Architectural decisions

Reliability/Observability

Cross-functional collaboration

Tools

Pinecone

FAISS

Chroma

Job description

We're partnered with a high-growth, mission-driven SaaS company transforming how businesses build and maintain trust, with AI at the core of their next phase of innovation. The platform is redefining how critical enterprise workflows are automated, particularly in areas where reliability, auditability, and security are essential.

This is a high-impact role where you will help define how AI is architected across the company. You won't just be building features. You'll make foundational decisions around systems, evaluation, and long-term technical direction, working across LLMs, retrieval systems, and agent-based workflows in production environments.

What You'll Do

• Design and own production AI systems end-to-end, including LLM pipelines, retrieval systems, and orchestration layers
• Build and scale RAG systems, reranking pipelines, and vector-based search infrastructure
• Define evaluation frameworks to measure retrieval quality, reasoning accuracy, and system performance
• Analyze production behavior, identify failure modes, and drive improvements based on data
• Make key architectural decisions across model infrastructure, tooling, and workflows
• Partner closely with product, platform, and domain teams to translate complex requirements into scalable systems
• Lead best practices for building reliable, observable, and cost-efficient AI systems

Requirements

• 6+ years of software engineering experience, including 3+ years working on ML or AI systems
• Proven experience owning and deploying production LLM systems
• Strong background in RAG, embeddings, reranking, and vector databases (e.g., Pinecone, FAISS, Chroma)
• Experience designing evaluation systems and improving models through quantitative analysis
• Strong Python skills, with solid software engineering fundamentals
• Experience making architectural decisions that influence team or org direction
• Strong understanding of production systems, including reliability, observability, and cost tradeoffs
• Ability to break down ambiguous problems and operate with a high degree of ownership
• Clear communication skills and experience working cross-functionally

Nice to Have

• Experience in regulated domains such as compliance or security
• Familiarity with data platforms or analytics tooling
• Experience with orchestration frameworks (e.g., Temporal, Airflow)
• Exposure to LLM evaluation platforms or tooling
• Contributions to open source, research, or technical communities

If you're interested in shaping how AI systems are built, evaluated, and deployed in high-trust environments, this is an opportunity to have direct influence on both technical direction and real-world impact at a fast-growing company.