Lead ML Engineer - Semantic AI & Knowledge Graphs

Insight Global; Newark (NJ); On-site; USD 140,000 - 200,000; Full time

Job summary

Insight Global is hiring an experienced Machine Learning Engineer to lead the engineering and deployment of machine learning and agentic AI solutions for a major financial services client. You will architect semantic knowledge layers, knowledge graphs, and Graph RAG capabilities to enable LLMs and AI agents to reason over complex business data.

You will develop data ingestion, retrieval, memory, and semantic pipelines and build applications using Python, SQL, and modern ML frameworks.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, or a related technical field.
• Strong experience developing and deploying machine learning and Agentic AI solutions in production environments.
• Strong experience with knowledge graphs.
• Hands-on experience with LLMs, multi-agent systems, orchestration frameworks, memory management, guardrails, human-in-the-loop controls, and agent observability.
• Strong software engineering fundamentals, including object-oriented programming, system design, microservices, REST APIs, distributed computing, version control, and testing.
• Experience with Python and SQL, along with modern AI/ML frameworks such as PyTorch, TensorFlow, or scikit-learn.
• Experience designing and implementing scalable data ingestion, transformation, retrieval, and processing pipelines.
• Understanding of data governance, schema evolution, data lineage, ontology lifecycle management, and enterprise data architecture.
• Experience with CI/CD pipelines, automated testing, model deployment, model versioning, and production monitoring.
• Experience with semantic layers, ontologies, and Graph RAG architectures.
• Experience with AI testing, evaluation frameworks, or harness engineering.
• Experience with cloud-native AI/ML platforms and services such as AWS, Azure, GCP, or SageMaker.
• Knowledge of relational, NoSQL, and graph databases, including semantic data models.

Responsibilities

• Architect semantic knowledge layers, knowledge graphs, and Graph RAG capabilities enabling LLMs and AI agents to reason over complex business data and relationships.
• Develop data ingestion, retrieval, memory, and semantic pipelines.
• Build and optimize applications using Python, SQL, and modern LLM frameworks.
• Leverage cloud technologies to deploy AI solutions at scale.
• Provide technical leadership and drive innovation across AI initiatives.
• Support ontology design, data governance, schema evolution, data lineage, CI/CD automation, and enterprise system integration.

Skills

Knowledge graphs

LLMs

Multi-agent systems

Orchestration frameworks

Memory management

Guardrails

Human-in-the-loop

Agent observability

OOP

System design

Microservices

REST APIs

Distributed computing

Version control

Testing

Python

SQL

PyTorch

TensorFlow

scikit-learn

Data pipelines

Data governance

Schema evolution

Data lineage

Ontology lifecycle

CI/CD

Automated testing

Model deployment

Model versioning

Production monitoring

Semantic layers

Ontologies

Graph RAG

AI testing

Evaluation frameworks

Harness engineering

AWS

Azure

GCP

SageMaker

RDBMS

NoSQL

Graph databases

Semantic data models

Education

Bachelor’s degree in Computer Science, Engineering, or a related technical field

Master’s degree in Computer Science, Engineering, Data Science, or a related field

Tools

Python

SQL

PyTorch

TensorFlow

scikit-learn

AWS

Azure

GCP

SageMaker

Job description

Insight Global is hiring an experienced Machine Learning Engineer to lead the engineering and deployment of machine learning and agentic AI solutions for a major financial services client. You will architect semantic knowledge layers, knowledge graphs, and Graph RAG capabilities to enable LLMs and AI agents to reason over complex business data.

You will develop data ingestion, retrieval, memory, and semantic pipelines and build applications using Python, SQL, and modern ML frameworks.