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Foundational AI Engineer
Foundational AI Engineer
Overview
Application
Location
Remote / USA / India
Employment Type
Full-Time
Location Type
Remote
Department
Engineering
About Rive
Rive is building the Agentic Operating System for Industrial Assets, a platform where asset data, processes, and AI agents work together to orchestrate maintenance, reliability, and supply-chain decisions automatically. We unify fragmented industrial data into a configurable ontology and knowledge graph, and empower customers with autonomous AI agents that continuously enrich data quality, improve reliability, and drive operational outcomes.
We are currently in stealth mode, backed by strong early customer traction across manufacturing, fleet, utilities, and heavy industries.
Role Overview
As a Foundational AI Engineer, you will build the core intelligence layer that powers Rive’s autonomous agents, knowledge graph computations, and industrial reasoning workflows. You will work at the intersection of AI models, graph data structures, MLOps, embeddings, retrieval, and agentic orchestration, ensuring that Rive’s platform can reason across millions of assets, parts, documents, failures, and maintenance events in real time.
You will own the design and implementation of AI systems that:
Extract structure from unstructured data
Generate attributes, classifications, and relationships for assets
Perform entity matching, deduplication, anomaly detection
Power the autonomous
Reason over the knowledge graph to produce insights and actions
This is a role for someone who enjoys building foundational intelligence, not just model wrappers.
Key Responsibilities
1. Core Industrial AI & Graph Reasoning
Build and optimize algorithms for entity resolution, deduplication, classification inference, attribute prediction, and relationship discovery.
Develop graph-based reasoning pipelines for traversing and computing insights across Rive’s ontology.
Implement vector stores, embedding pipelines, and retrieval models tailored to industrial domain data.
2. Autonomous Agent Intelligence
Build foundational capabilities for Rive’s “agent layer” including:
data quality improvement agents
asset enrichment agents
business partner news agent
spare parts deduplication agent
workflow/decision agents
Implement context windows, memory, and reasoning loops for safe and deterministic agent behavior.
3. Unstructured Data Understanding
Create models for parsing information from:
PDFs, manuals, data sheets
nameplates, images, P&IDs
maintenance logs, fault codes
emails, spreadsheets
Structure this data into assets, parts, specs, and relationships with confidence scores.
4. MLOps & Productization
Deploy and manage large-scale inference workloads on cloud-native infrastructure (e.g., AWS/GCP/Azure).
Establish monitoring, data drift detection, and continuous improvement loops.
Balance accuracy, cost, and latency for production-grade AI systems.
Required Skills & Experience
Technical
Strong foundation in machine learning, deep learning, and applied AI.
Experience with LLMs, embedding models, RAG systems, and agent frameworks.
Proficiency in Python, PyTorch / TensorFlow, and modern ML tooling.
Experience with graph databases (Neo4j, Neptune, TigerGraph) or knowledge graph systems.
Hands-on experience in entity matching, ontology-based reasoning, NLP, and document intelligence.
Experience deploying models in production (Docker, Kubernetes, cloud platforms).
Preferred
Experience with industrial data, asset management, or maintenance systems (SAP PM, Maximo, Infor, Hexagon).
Experience with MLOps platforms (Weights & Biases, MLflow, SageMaker, Vertex AI).
Experience building autonomous agents or tool-using model architectures.
Background in information retrieval, search, or ETL pipelines.
Soft Skills
Ability to think from first principles and work in ambiguity.
Strong ownership — able to take a concept from idea → prototype → production.
Passion for shaping early-stage product and influencing architecture.
Low-ego, collaborative, pragmatic, and delivery oriented.
What You’ll Get
Build the core intelligence of a category-defining product
Ability to influence architecture and product direction from day one
Work with a global founding team (ex-SAP, industrial, AI systems experts)
Fast learning environment with direct access to real industrial data
Competitive equity and compensation package
Opportunity to create impact across manufacturing, logistics, energy, and fleet operations at global scale
Why This Role Is Exciting
You will help build the AI brain of Rive — the piece that transforms raw industrial chaos into an intelligent, self-improving operational network. Your work will directly power the autonomous agents that change how factories, fleets, and infrastructure operate.
Apply for this Job
Overview
Application
Location
Remote / USA / India
Employment Type
Full-Time
Location Type
Remote
Department
Engineering
About Rive
Rive is building the Agentic Operating System for Industrial Assets, a platform where asset data, processes, and AI agents work together to orchestrate maintenance, reliability, and supply-chain decisions automatically. We unify fragmented industrial data into a configurable ontology and knowledge graph, and empower customers with autonomous AI agents that continuously enrich data quality, improve reliability, and drive operational outcomes.
We are currently in stealth mode, backed by strong early customer traction across manufacturing, fleet, utilities, and heavy industries.
Role Overview
As a Foundational AI Engineer, you will build the core intelligence layer that powers Rive’s autonomous agents, knowledge graph computations, and industrial reasoning workflows. You will work at the intersection of AI models, graph data structures, MLOps, embeddings, retrieval, and agentic orchestration, ensuring that Rive’s platform can reason across millions of assets, parts, documents, failures, and maintenance events in real time.
You will own the design and implementation of AI systems that:
Extract structure from unstructured data
Generate attributes, classifications, and relationships for assets
Perform entity matching, deduplication, anomaly detection
Power the autonomous
Reason over the knowledge graph to produce insights and actions
This is a role for someone who enjoys building foundational intelligence, not just model wrappers.
Key Responsibilities
1. Core Industrial AI & Graph Reasoning
Build and optimize algorithms for entity resolution, deduplication, classification inference, attribute prediction, and relationship discovery.
Develop graph-based reasoning pipelines for traversing and computing insights across Rive’s ontology.
Implement vector stores, embedding pipelines, and retrieval models tailored to industrial domain data.
2. Autonomous Agent Intelligence
Build foundational capabilities for Rive’s “agent layer” including:
data quality improvement agents
asset enrichment agents
business partner news agent
spare parts deduplication agent
workflow/decision agents
Implement context windows, memory, and reasoning loops for safe and deterministic agent behavior.
3. Unstructured Data Understanding
Create models for parsing information from:
PDFs, manuals, data sheets
nameplates, images, P&IDs
maintenance logs, fault codes
emails, spreadsheets
Structure this data into assets, parts, specs, and relationships with confidence scores.
4. MLOps & Productization
Deploy and manage large-scale inference workloads on cloud-native infrastructure (e.g., AWS/GCP/Azure).
Establish monitoring, data drift detection, and continuous improvement loops.
Balance accuracy, cost, and latency for production-grade AI systems.
Required Skills & Experience
Technical
Strong foundation in machine learning, deep learning, and applied AI.
Experience with LLMs, embedding models, RAG systems, and agent frameworks.
Proficiency in Python, PyTorch / TensorFlow, and modern ML tooling.
Experience with graph databases (Neo4j, Neptune, TigerGraph) or knowledge graph systems.
Hands-on experience in entity matching, ontology-based reasoning, NLP, and document intelligence.
Experience deploying models in production (Docker, Kubernetes, cloud platforms).
Preferred
Experience with industrial data, asset management, or maintenance systems (SAP PM, Maximo, Infor, Hexagon).
Experience with MLOps platforms (Weights & Biases, MLflow, SageMaker, Vertex AI).
Experience building autonomous agents or tool-using model architectures.
Background in information retrieval, search, or ETL pipelines.
Soft Skills
Ability to think from first principles and work in ambiguity.
Strong ownership — able to take a concept from idea → prototype → production.
Passion for shaping early-stage product and influencing architecture.
Low-ego, collaborative, pragmatic, and delivery oriented.
What You’ll Get
Build the core intelligence of a category-defining product
Ability to influence architecture and product direction from day one
Work with a global founding team (ex-SAP, industrial, AI systems experts)
Fast learning environment with direct access to real industrial data
Competitive equity and compensation package
Opportunity to create impact across manufacturing, logistics, energy, and fleet operations at global scale
Why This Role Is Exciting
You will help build the AI brain of Rive — the piece that transforms raw industrial chaos into an intelligent, self-improving operational network. Your work will directly power the autonomous agents that change how factories, fleets, and infrastructure operate.
Apply for this Job
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©2026 Rive. All rights reserved.
©2026 Rive. All rights reserved.