✦ Luna Orbit — AI & Machine Learning

Specialist, Data Science

at Northern Trust

📍 2 Locations Unknown Posted March 29, 2026
Type Not Specified
Experience senior
Exp. Years 7-8 years
Education Not specified
Category AI & Machine Learning

Senior AI/ML engineer role focused on designing and deploying agentic AI systems and RAG-based pipelines for financial services. The role requires building production-grade ML pipelines, collaborating across teams, and deploying AI infrastructure on cloud platforms.

  • Design, build, and optimize agentic AI systems
  • Architect and deploy RAG pipelines
  • Build and maintain end-to-end ML pipelines; EDA; model training; deployment
  • Develop production-grade APIs and microservices for AI systems
  • Collaborate across teams for tool integrations and multi-agent coordination

Stack includes Python with PyTorch/TensorFlow, LangChain/LangGraph, LlamaIndex; integration with OpenAI, Azure OpenAI, Anthropic; RAG components, vector databases, PostgreSQL; containerized with Docker and Kubernetes; deployment with CI/CD and MLOps.

The ideal candidate is a senior AI/ML engineer with 7-8 years of experience building production-grade ML pipelines, expertise in LLMs, RAG, and multi-agent AI systems. Proficient in Python and modern ML frameworks with strong cloud experience (Azure AWS GCP) and a track record deploying AI systems in production.

7-8 years of industry experience in AI/ML engineeringdata scienceor fullstack AI solution architecturePythonPyTorchTensorFlowLangChain/LangGraphLlamaIndexHugging FaceScikit-learnLLMsembeddingsvector searchand RAG best practicesAgentic AI architecturestool-callingand autonomous agent designCloud-based AI services (Azure AIAWS SageMakerGCP Vertex AI)SparkSQLETLDockerKubernetesserverless architectures
evaluation frameworks (e.g.RAGASLangSmithDeepEval)knowledge graph augmentation for enterprise RAGmulti-modal AI systems (visionspeechstructured data)data privacycomplianceand AI safety practices
LangGraphAutoGenHaystack AgentsReAct-style agentsOpenAIAzure OpenAIAnthropicPostgreSQLPostgressVertex AISageMaker
7-8 years of AI/ML engineeringdata scienceor fullstack AI solution architecturePythonPyTorchTensorFlowLangChainLangGraphLlamaIndexHugging FaceScikit-learnLLMsembeddingsvector searchRAGagentic AI architecturestool-callingautonomous agent designAzure AIAWS SageMakerGCP Vertex AISparkSQLETLDockerKubernetesserverlessCI/CDMLOpsOpenAIAzure OpenAIAnthropicPostgreSQLPostgress
PythonPyTorchTensorFlowLangChainLangChain/LangGraphLangGraphLlamaIndexHugging FaceScikit-learnLLMsembeddingsvector searchRAGOpenAIAzure OpenAIAnthropicopen-source modelsPostgreSQLPostgressML pipelinesSparkSQLETLDockerKubernetesserverlessCI/CDMLOpsOpen-source modelsPostgreSQL
problem-solvinganalyticalcommunicationdocumentationcross-functional collaborationownership
Industry Banking
Job Function Develop AI systems and ML pipelines for production-scale applications with RAG and agentic architectures
Role Subtype ML Engineer
Tech Domains Python, TensorFlow, PyTorch, SQL / PostgreSQL, Docker, Kubernetes, Amazon Web Services, Microsoft Azure, Google Cloud Platform, Spark
agentic aiagent frameworkslanggraphautogenhaystack agentsreactragvector databasepostgresqlpostgresopenaiazure openaianthropicopen-source modelspythonpytorchtensorflowlangchainllamaindexhugging facescikit-learnvector searchaws sageMakergoogle cloud platformvertex aidockerkubernetesci/cdmlopsmodel governance

Less than 7 years of relevant AI/ML experience, Lack of Python proficiency, No experience with cloud AI services (Azure AWS GCP), No exposure to LLMs or RAG

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