JAGGAER

Architect, Data Scientist, Data & AI Team

JAGGAER

full-time

Posted on:

Origin:  • 🇺🇸 United States • North Carolina

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Job Level

SeniorLead

Tech Stack

AirflowAmazon RedshiftAWSAzureDockerERPETLGoGoogle Cloud PlatformIoTKubernetesLinuxOpen SourcePandasPySparkPythonPyTorchRedisSQLTableauTensorflowTerraform

About the role

  • Data & Feature Engineering: Build scalable ingestion, ETL/ELT, and feature store pipelines across OpenSearch, Snowflake, Redshift, and Redis.
  • Design semantic layers and vector indexes (Pinecone, OpenSearch) that power retrieval augmented generation (RAG) and Agentic AI workflows.
  • Model Development & Experimentation: Prototype, train, and evaluate predictive, prescriptive, and generative models in Amazon SageMaker (plus open source frameworks).
  • Implement automated A/B tests and champion/challenger experiments; translate findings into product requirements.
  • ML / LLM Ops: Own CI/CD, monitoring, drift detection, and scalable inference for classical ML and LLM pipelines.
  • Package models and agents into reusable micro services with Terraform / Docker / Kubernetes.
  • Agentic Platform Integration: Orchestrate multi-agent task flows (LangGraph, CrewAI, or equivalent) that call JAGGAER and third-party APIs.
  • Collaborate with front-end teams to embed real-time analytics and AI insights into customer-facing apps.
  • Insight Generation & Storytelling: Diagnose customer data issues; deliver visual analyses (Tableau, Superset, Streamlit, or R/Python) for executives and non-technical stakeholders.
  • Champion data-driven decision making across Product, Services, and Go to Market teams.

Requirements

  • Bachelor’s or Master’s in Computer Science, Statistics, Math, Data Science, or related field.
  • 10+ years designing and deploying production-grade ML or data engineering solutions.
  • Proficiency in Python (Pandas, PySpark, scikit learn, TensorFlow/PyTorch) and SQL.
  • Hands-on work with at least two of the following platforms: OpenSearch, Snowflake, Redshift, Redis, Pinecone, SageMaker.
  • Solid grounding in statistical modeling, supervised/unsupervised ML, and evaluation metrics.
  • Experience with Linux, Git, CI/CD, Docker, and at least one orchestration framework (Airflow, Prefect, Kubeflow, or Dagster).
  • Clear, concise communicator able to present complex analyses to senior leadership.
  • Preferred: LLM fine tuning, prompt engineering, or RAG pipelines; experience deploying ML services on AWS/Azure/GCP; knowledge of procurement, supply chain, IoT sensor, or ERP data; familiarity with LangChain, CrewAI, Haystack; track record of hackathons/open-source/published research.