Senior AI/ML Engineer - Enterprise Agents(Hybrid, San Mateo)

Acceler8 Talent; San Mateo (CA); Hybrid; USD 213,000 - 288,000; Full time

Benefits offered by this job

Equity

Hybrid work

Competitive salary

Job summary

Acceler8 Talent in San Mateo is seeking an AI/ML Engineer to own the intelligence layer behind a production AI agent platform. You will architect multi-step agents, retrieval pipelines, and code-generation components that produce reliable data pipelines for enterprise use.

The role emphasizes evaluation loops, model selection for performance and cost, and end-to-end reliability. This is a hands-on position with ownership of core architectural decisions in a regulated enterprise context.

Qualifications

• 4+ years of software-engineering experience with meaningful AI/ML depth.
• Experience building agents, coding assistants, or generative-AI systems in production.
• Strong Python skills.
• Hands-on experience with RAG, retrieval, code generation, or knowledge systems.

Responsibilities

• Own the intelligence layer behind the product, across agent architecture, retrieval, code generation, evaluation, and production reliability.
• Design multi-step agents for planning, retrieval, generation, and error recovery.
• Build evaluation systems and improvement loops for agent quality.
• Develop RAG pipelines using vector search, knowledge graphs, lineage, and metadata.
• Generate and validate production-grade data transformations.
• Select and adapt models for quality, latency, throughput, and cost.
• Lead production deployment of enterprise AI agents.

Skills

Python

AI/ML depth

Agents / AI assistants

Retrieval (RAG)

Code generation

Tools

Neo4j

AWS

Kubernetes

Databricks

Snowflake

Job description

Acceler8 Talent in San Mateo is seeking an AI/ML Engineer to own the intelligence layer behind a production AI agent platform. You will architect multi-step agents, retrieval pipelines, and code-generation components that produce reliable data pipelines for enterprise use.

The role emphasizes evaluation loops, model selection for performance and cost, and end-to-end reliability. This is a hands-on position with ownership of core architectural decisions in a regulated enterprise context.