Archived listing. The details below describe a past opening.
Required Skills & Experience
- 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
Nice to Have Skills & Experience
- 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
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.