Senior AI Engineer

About the role

Emergence is a thematic holding company backed by the Pritzker Organization focused exclusively on acquiring and scaling category-defining software businesses. We invest in focused portfolios, specialized operating groups with deep domain expertise and proven playbooks. Emergence combines operational rigor with a growth equity mindset, driving sustainable ARR growth, profitability improvements, and industry-leading customer outcomes. The Mission Design and deploy production AI systems that integrate cleanly across multiple backend services, enabling portfolio companies to embed AI at scale.

Responsibilities

• 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

Additional Information

Senior AI Engineer - Systems & Integration, Emergence | India - Remote | Full-Time

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