Staff Forward-Deployed AI Engineer (Hybrid, Client-Facing)

Fdetimes; New York, San Francisco (NY, CA); Hybrid; USD 210.000 - 300.000; Vollzeit

Zusammenfassung

Invisible is seeking a Staff Forward Deployed Engineer to lead high-impact AI-powered solutions for enterprise clients. You will shape strategy, architecture, and end-to-end delivery, blending hands-on ML engineering with technical consulting to solve complex workflows.

You’ll work on LLM-powered systems, RAG pipelines, and scalable ML architectures while coaching mid-level engineers and collaborating with executives and clients.

Qualifikationen

• 8+ years of software engineering experience, including significant time spent building data, ML, or backend systems.
• Python & ML/LLM Frameworks: Deep proficiency in Python with hands-on experience using Hugging Face, LangChain, OpenAI, Pinecone, and related ecosystems, including LLMs and AI Agents
• Deployment & Infrastructure: Skilled in full-stack and API-based deployment patterns, including Docker, FastAPI, Kubernetes, and cloud environments (GCP, AWS, Azure)
• Platform Orchestration: Experienced with workflow orchestration libraries, pub/sub systems (Kafka), and schema governance
• Data Management: 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
• ML Operations (for teams with an applied AI/ML focus): Experience with MLOps frameworks and best practices - model versioning, monitoring, and lifecycle management - and designing scalable ML systems such as RAG pipelines
• 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
• Travel: Willingness to travel for on-site client engagements as needed
• Onsite: Available to work from the office a few days per week, depending on team needs - some NYC-based teams require 3 days per week onsite; your specific requirement will depend on the team you're placed with

Aufgaben

• Partner with delivery and executive stakeholders to scope, design, and lead implementation of AI-driven solutions, including LLM and agentic systems
• 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 - including for ML/AI-specific systems like RAG pipelines and production-grade AI applications
• 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, including MLOps practices for model versioning, monitoring, and lifecycle management
• Coach and mentor mid-level engineers and help shape the culture of forward-deployed AI engineering at Invisible

Kenntnisse

8+ years of software engineering exp.

Python & ML/LLM Frameworks

Deployment & Infra

Platform Orchestration

Data Management

ML Operations

Product & System Design

Messy data handling

Communication & Client-facing

Project ownership

On-call readiness

Willing to travel

Onsite NYC collaboration

Tools

Docker

FastAPI

Kubernetes

Cloud: GCP/AWS/Azure

LangChain

Hugging Face

OpenAI

Pinecone

Kafka

Jobbeschreibung

Invisible is seeking a Staff Forward Deployed Engineer to lead high-impact AI-powered solutions for enterprise clients. You will shape strategy, architecture, and end-to-end delivery, blending hands-on ML engineering with technical consulting to solve complex workflows.

You’ll work on LLM-powered systems, RAG pipelines, and scalable ML architectures while coaching mid-level engineers and collaborating with executives and clients.