Lead Machine Learning Engineer

Insight Global
Newark, NJ, United States
3 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Working hours
Regular working hours
Job source

Tech stack

Artificial Intelligence Amazon Web Services Automation of Tests Microsoft Azure Cloud Computing Cloud Engineering Continuous Integration Data Architecture Data Governance Distributed Systems Memory Management Graph Database
+25 more
Python (Programming Language) Machine Learning NoSQL Object-Oriented Software Development Tensorflow Azure Machine Learning SQL Databases Google Cloud Data Ingestion Pytorch Large Language Models Multi-Agent Systems Data Layers Scikit Learn Kubernetes Information Technology Data Lineage Machine Learning Operations Virtual Agents Restful APIs GPT Software Version Control Data Pipelines Devsecops Microservices

Job description

Insight Global is hiring an experienced Machine Learning Engineer to support one of its largest financial services clients. This individual will lead the engineering and deployment of machine learning and agentic AI solutions while partnering with Data Scientists, Data Engineers, Data Analysts, DevSecOps, and other cross-functional teams to deliver scalable, production-ready systems. Responsibilities include architecting semantic knowledge layers, knowledge graphs, and Graph RAG capabilities that enable large language models and AI agents to reason over complex business data and relationships; developing data ingestion, retrieval, memory, and semantic pipelines; building and optimizing applications using Python, SQL, and modern LLM frameworks; and leveraging cloud technologies to deploy AI solutions at scale. The role will also support ontology design, data governance, schema evolution, data lineage, CI/CD automation, and enterprise system integration while providing technical leadership and driving innovation across AI initiatives.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global’s Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.

Requirements

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

  • 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

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