Emergence
Technology
SeniorAIEngineer
Neural analysis suggests this role is
optimal for Senior candidates.
“Senior AI Engineer at Emergence. Skills: AI integration, LLM applications, Vector databases, ML infrastructure. Design integration architectures. Build reusable ML infrastructure”
What You'll Achieve.
Embed AI at scale; Reduce integration time; Standardize tooling; Improve reproducibility; Measurable quality improvements
Industry & Context.
Identify inefficient patterns; Design reusable abstractions; Reduce technical debt
What They're Looking For.
Must Have
5+ years backend systems, 5+ years third-party tools, 5+ years API integrations, Python skills, SQL skills, Production LLM applications, Production vector search systems, Production ML inference pipelines, Production automated workflows, Integrate external AI tools, Monitored systems, Versioned systems, Reproducible systems
Nice to Have
MLOps platforms experience, ML infrastructure tooling experience, Kubernetes familiarity, Docker familiarity, Cloud deployment familiarity, Retrieval-augmented generation systems experience, Scaling prompt engineering experience
What You'll Do.
Design integration architectures
Build reusable ML infrastructure
Establish AI system integration best practices
Establish governance patterns
Own system design reviews
Optimize production AI systems
Mentor engineers on best practices
Mentor engineers on reproducibility
Mentor engineers on monitoring
Mentor engineers on safe deployment
How You'll Work.
Team & Collaboration
Multiple backend services; Multiple portfolio companies; Dependent teams
Full Job Description
Senior AI Engineer - Systems & Integration, Emergence | India - Remote | Full-Time Who We Are 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. What You'll Do - 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. What We're Looking For Must-haves - 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 prod
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