Partner with delivery and executive stakeholders to scope, design, and lead implementation of AI-driven solutions
Identify transformational opportunities in messy, ambiguous workflows and turn them into repeatable systems
Lead architecture design and trade-off discussions across performance, scalability, cost, and reliability
Own projects from first discovery call through full deployment — including client-facing delivery, internal coordination, and post-launch iteration
Build shared infrastructure, reusable components, and internal playbooks to level-up the team
Coach and mentor mid-level engineers and help shape the culture of forward-deployed AI engineering at Invisible
Requirements
12+ years of software engineering experience, including significant time spent building data, ML, or backend systems
Deep proficiency in Python with hands-on experience using Hugging Face, LangChain, OpenAI, Pinecone, and related ecosystems
Skilled in full-stack and API-based deployment patterns, including Docker, FastAPI, Kubernetes, and cloud environments (GCP, AWS)
Experienced with workflow orchestration libraries, pub/sub systems (Kafka), and schema governance
Expertise in data governance and operations, including Unity Catalog and policy management, cluster/job orchestration, data contracts and quality enforcement, Delta/ETL pipelines, and replay processes
Strong product and system design instincts — you understand business needs and how to translate them into technical architecture
Experience building usable systems from messy data and ambiguous requirements
Excellent communication and client-facing skills; you’ve led conversations with technical and non-technical stakeholders alike
Proven experience owning projects from scoping through deployment in ambiguous, high-stakes environments
Be willing to be on-call for our customers when situations arise
Ability to travel roughly 25–50% of the time, sometimes short-notice trips—primarily across North America with occasional international roll-outs—to work directly on-site with clients
Strong engineering background demonstrated by a Bachelor’s degree in Data Science, Computer Science and related fields OR equivalent professional experience.
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