Operations Engineer

Boostlingo

6d•Remote

About The Position

We are seeking a Senior Engineering Manager to lead Boostlingo’s AI engineering organization, spanning: AI Core – production-grade AI platforms and services AI Discovery (R&D / POC) – experimentation, prototyping, and validation You will lead through engineering managers, not replace them. This role is for a technical engineering leader, someone with strong architectural judgment and AI system literacy, who focuses primarily on: People leadership Technical direction Execution excellence Organizational health You are not expected to be the deepest AI expert in the room. You are expected to know enough to: Ask the right questions Make sound tradeoffs Hold teams accountable to production standards This role reports to the Senior Director of Engineering.

Requirements

15+ years of professional software engineering experience
4+ years leading senior engineers and/or engineering managers
Proven experience delivering complex systems into production
Experience leading and collaborating multiple teams with different mandates (platform vs R&D)
Strong track record balancing innovation with reliability and compliance
Experience operating in regulated or compliance-sensitive environments
Distributed systems and cloud-native architectures
APIs, microservices, and event-driven systems
AI/ML systems at the architectural level (not model research)
Model integration via third-party providers (e.g., OpenAI, Anthropic, etc.)
Observability and cost awareness in AI-powered systems

Nice To Haves

Experience managing AI-related incidents or cost overruns
Vendor management and build-vs-buy decision-making
Prior ownership of platform modernization efforts
Bachelor’s or Master’s degree in CS or related field

Responsibilities

Lead multiple teams via Engineering Managers
Set clear expectations, operating principles, and success metrics
Coach managers on execution, technical leadership, and people management
Develop senior ICs into technical leaders
Build a culture of ownership, accountability, and pragmatic decision-making
Partner with People Ops on hiring, performance, and succession planning
Provide architectural guidance for AI platforms and services
Ensure clear separation between: AI Discovery / experimentation AI Core / production systems
Guide decisions around: Model integration patterns (hosted APIs, orchestration, evaluation) Inference pipelines and system boundaries Reliability, observability, and failure modes
Partner with Architecture and Security to ensure AI systems are: Scalable Secure Compliant (PII/PHI where applicable) Cost-conscious
Own delivery outcomes across AI initiatives
Translate strategy into shippable, incremental milestones
Remove cross-team execution bottlenecks
Hold teams accountable to quality, timelines, and operational readiness
Define and track metrics for: System reliability Latency and performance Cost and usage Team health and delivery predictability
Partner closely with Product, Architecture, Security, Legal, and Platform teams
Communicate progress, risks, and tradeoffs clearly to executive leadership
Represent AI engineering in planning, roadmap, and investment discussions
Ensure AI features are production-ready before broad rollout