Senior AI Engineer

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Emergence

WebsiteLinkedInAll Job Openings

11 - 50 employees

💸 Finance

🤝 B2B

☁️ SaaS

Finance • B2B • SaaS

Emergence is a venture capital firm that focuses on investing in early- and growth-stage enterprise software and cloud/SaaS companies. It partners with founders building B2B platforms and services, providing capital and operational support to help scale businesses in the enterprise software space.

📋 Description

• Design end-to-end AI integration architectures connecting LLM APIs, vector databases, and inference systems to existing backend infrastructure. • Build reusable ML infrastructure components like feature pipelines, model serving layers, and evaluation frameworks that multiple portfolio companies standardize on. • Establish AI system integration best practices and governance patterns that become repeatable playbooks across the holding company. • Own system design reviews for AI initiatives across portfolio companies, identifying bottlenecks and recommending architectural improvements. • Optimize production AI systems for cost and latency by profiling pipelines, implementing compression, and right-sizing compute infrastructure. • Mentor engineers at portfolio companies on production AI best practices, reproducibility, monitoring, and safe deployment patterns.

🎯 Requirements

• 5+ years building backend systems or integrations with hands-on experience connecting multiple third-party tools and APIs in production. • Proven track record architecting system integrations at scale that reduced integration time or standardized tooling across teams. • Strong Python and SQL skills for building data pipelines and backend services that feed AI systems. • Hands-on production experience deploying LLM applications, vector search systems, ML inference pipelines, or automated workflows. • Deep understanding of integrating external AI tools into existing backend architectures without requiring core system rearchitecture. • Built systems that are monitored, versioned, and reproducible, not one-off prototypes or experiments. • Experience with MLOps platforms like MLflow, Weights & Biases, or SageMaker, or ML infrastructure tooling. • Familiarity with Kubernetes, Docker, or cloud deployment on AWS, GCP, or Azure for containerizing AI services. • Experience building retrieval-augmented generation systems or scaling prompt engineering across teams.

🏖️ Benefits

• Remote work from India with flexibility on location. • Professional development budget and conference attendance. • Work directly with multiple portfolio companies to shape how AI scales across a holding company.