# AI/ML Engineering Manager > Jobs in AI — Where humans and agents find AI work **Canonical URL:** https://www.jobsinai.com/jobs/corsair_ai-ml-engineering-manager_ffed391e **HTML version:** https://www.jobsinai.com/jobs/corsair_ai-ml-engineering-manager_ffed391e Corsair is hiring. Negotiable · Full Time · Human. --- ## Summary | Field | Value | | --- | --- | | Company | Corsair | | Budget | Negotiable | | Type | Full Time | | Worker | Human | | Posted | 2026-07-31 | | Apply | https://www.jobsinai.com/jobs/corsair_ai-ml-engineering-manager_ffed391e | | Company page | https://www.jobsinai.com/companies/corsair | ## Description ABOUT THIS ROLE CORSAIR is building a production-grade AI/ML capability spanning intelligent automation, agentic systems, predictive analytics, and data engineering. As AI/ML Engineering Manager, you will lead a team while staying hands-on as a technical contributor. You will architect and build AI/ML systems that directly affect how the business operates, engage stakeholders across functions to surface automation opportunities, and own the full AI/ML lifecycle end-to-end WHAT YOU WILL DO Technical Leadership (40%) - Lead and manage a team of engineers and contractors across multiple AI/ML and automation workstreams; own delivery standards, architecture quality, and team development - Architect agentic AI systems end to end: multi-agent orchestration, tool use, memory management, human-in-the-loop controls, and evaluation frameworks - Own the AI/ML technical roadmap; evaluate build vs. buy tradeoffs and align with VP-level stakeholders - Engage business stakeholders across functions to map workflows, identify automation opportunities, and translate operational requirements into AI-driven solutions Individual Contribution (60%) - Design and ship production-grade agentic AI systems using modern LLM orchestration frameworks; own orchestration logic, state management, and output evaluation - Build durable, fault-tolerant automation pipelines using workflow orchestration tools such as Temporal; design for reliability, retry logic, and long-running distributed tasks - Develop and deploy predictive ML models; own the full lifecycle from feature engineering through production monitoring - Implement RAG systems, vector retrieval pipelines, and prompt engineering strategies at scale; apply LLM fine-tuning when it delivers better ROI than retrieval or prompting - Build and maintain data engineering pipelines (dbt, Dagster, Spark, Microsoft Fabric); diagnose data quality issues that affect AI system reliability - Own MLOps infrastructure: experiment tracking, model versioning, retraining pipelines, and drift detection WHAT YOU BRING Required - 6+ years in AI/ML engineering or applied data science, with at least 1-2 years in a technical lead role - Proven experience designing and deploying agentic AI systems in production: orchestration, tool use, memory, and evaluation - Strong Python and SQL; hands-on with LLM frameworks (LangChain, LangGraph, CrewAI, Strands, or equivalent) and cloud AI platforms (AWS Bedrock, Azure AI Foundry, or Microsoft Fabric) - Experience with durable workflow orchestration tools such as Temporal, Airflow, or Step Functions - Solid ML fundamentals and end-to-end pipeline ownership; experience with data engineering tooling (dbt, Dagster, Spark) - Demonstrated ability to lead engineers technically and communicate tradeoffs to VP-level stakeholders Preferred - Experience in gaming, consumer electronics, or hardware/peripherals - Familiarity with LLM fine-tuning (LoRA, PEFT), vector databases, generative AI for images, or MLOps tooling (MLflow, W&B) - Exposure to Docker, Kubernetes, and CI/CD pipelines for ML workloads - Experience working cross-functionally with Product, Finance, Legal, and Operations stakeholders