Agentic Delivery Engineer

This role focuses on implementing AI/ML solutions, including prompt engineering and RAG, within client environments. The engineer will contribute to integrating generative AI and LLM-based solutions into business processes and applications, and build data pipelines for AI/ML use. The role requires experience with generative AI tools and deploying LLM-powered solutions.

What you'd actually do

  1. Supporting the implementation of artificial intelligence / machine learning (AI/ML), analytics, and automation solutions within client environments
  2. Participating in prompt engineering, retrieval-augmented generation (RAG), and early-stage AI prototyping under the guidance of senior team members
  3. Contributing to the integration of AI/ML models, including generative artificial intelligence and large language model (LLM)-based solutions, into business processes, applications, and user workflows
  4. Building and maintaining data pipelines, including data ingestion, transformation, validation, and preparation for analysis or model use
  5. Applying software engineering and AI tools to support business needs

Skills

Required

  • Bachelor’s degree in Computer Science, Engineering, Data Science, Statistics, Mathematics, Information Systems, or a technical field
  • 1+ years of experience building reliable, maintainable, and well-documented code
  • 1+ years of experience with generative artificial intelligence tools, large language model application programming interfaces, prompt engineering, or retrieval-augmented generation applications
  • 1+ years of experience building and deploying generative artificial intelligence / large language model-powered solutions in client or production environments, including data preparation, testing, deployment, or monitoring
  • Ability to travel 0-25%, on average, based on the work you do and the clients and industries/sectors you serve.
  • Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future.

Nice to have

  • Experience with structured and/or unstructured data in a cloud or modern data environment such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP)
  • Experience with data and pipeline tools such as Databricks, Snowflake, BigQuery, dbt, or Airflow
  • Experience with business intelligence and visualization tools such as Power BI, Tableau, or Looker
  • Experience translating business or user requirements into technical workflows, outputs, or solution components
  • Experience with machine learning operations (MLOps) concepts such as model versioning, experiment tracking, or deployment workflows
  • Government consulting experience

What the JD emphasized

  • 1+ years of experience building and deploying generative artificial intelligence / large language model-powered solutions in client or production environments, including data preparation, testing, deployment, or monitoring

Other signals

  • implementation of AI/ML solutions
  • prompt engineering
  • retrieval-augmented generation (RAG)
  • integration of AI/ML models
  • building and deploying generative AI / LLM-powered solutions