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AI Summary
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 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.
Nice-to-haves
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.
Who you are
You've spent your career solving integration problems across backend systems. You identify inefficient patterns and design reusable abstractions that eliminate duplicate work. You expand your scope by owning problems that multiple teams depend on. You mentor others on production best practices without being asked. You set up monitoring and validation before shipping, and you refuse to compromise on quality even under deadline pressure. You solicit feedback from dependent teams before finalizing designs. You refactor inherited systems to reduce technical debt and improve reproducibility, leaving measurable quality improvements behind.
What We Offer
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.
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