- Company Name
- FlairX
- Job Title
- Principal Architect – Reinforcement Learning / Deep Learning Systems (RL/DL Focus)
- Job Description
-
**Job Title:** Principal Architect – Reinforcement Learning / Deep Learning Systems (RL/DL Focus)
**Role Summary:**
Lead the architectural design and development of scalable, distributed AI systems for multi‑agent path finding (MAPF) in robotics. Define technical direction, build RL/DL models from scratch, and drive end‑to‑end model pipelines and production deployment, collaborating across software, robotics, product, and operations teams.
**Expectations:**
- 12+ years total experience, with 7+ years in AI/ML system architecture and 2+ years in technical leadership.
- Deep expertise in reinforcement learning, deep learning, and real‑time multi‑agent path planning.
- Hands‑on delivery of ML models at scale using MLOps (Vertex AI or equivalents).
- Ability to architect distributed, event‑driven, containerized systems and integrate them with robotic orchestration.
**Key Responsibilities:**
- Architect distributed, real‑time ML systems for multi‑agent path planning.
- Design and implement full ML pipelines: data ingestion, training, validation, deployment, monitoring.
- Build and deploy RL/DL models that enable autonomous decision‑making at scale.
- Own production MLOps processes, including CI/CD, model versioning, and performance monitoring.
- Integrate ML pipelines with robotic control/orchestration engines for continuous learning.
- Collaborate with cross‑functional teams to align AI solutions with fulfillment and warehouse operations.
**Required Skills:**
- Strong knowledge of graph‑search, optimization, and path‑planning algorithms for multi‑agent systems.
- Expert in TensorFlow or PyTorch; proven RL/DL development and large‑scale deployment.
- Proficient in Python; familiarity with Erlang, Elixir, or similar concurrency‑first languages a plus.
- Experience with MLOps tools (Vertex AI, CI/CD, pipeline orchestration, model monitoring).
- Solid foundations in algorithms, operating systems, networking, memory management, and performance tuning.
- Distributed systems design, microservices, Docker, Kubernetes, and event‑driven architectures.
- Cloud platform experience, particularly Google Cloud Platform (GCP) and Vertex AI.
**Required Education & Certifications:**
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
- No specific certifications required; demonstrated technical leadership and project delivery suffice.
San francisco, United states
Hybrid
Senior
08-09-2025