Agentic Delivery Engineer

Deloitte Deloitte · Consulting

This role focuses on implementing AI/ML, analytics, and automation solutions for clients, with a specific emphasis on prompt engineering, RAG, and integrating generative AI and LLM-based solutions into business processes. The role involves building and maintaining data pipelines and applying software engineering principles to meet business needs within client environments.

What you'd actually do

  1. Support the implementation of AI/ML, analytics, and automation solutions within client environments
  2. Participate in prompt engineering, retrieval-augmented generation (RAG) approaches, and early-stage AI prototyping under the guidance of more senior team members
  3. Contribute to the integration of AI/ML models, including generative AI and LLM-based solutions, into business processes, applications, and user workflows
  4. Build and maintain data pipelines, including data ingestion, transformation, validation, and preparation for analysis or model use
  5. Apply your knowledge of software engineering and AI tools to satisfy business needs

Skills

Required

  • Bachelor’s degree in Computer Science, Engineering, Data Science, Statistics, Mathematics, Information Systems, or related technical field
  • 1+ years of experience building reliable, maintainable, and well-documented code
  • 1+ years of experience with generative AI tools, LLM APIs, prompt engineering, or RAG-based applications
  • 1+ years of experience building and deploying GenAI/LLM-powered solutions in client or production environments, demonstrating an understanding of the AI/ML lifecycle, including data preparation, testing, deployment, or monitoring
  • Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future
  • Ability to travel 0-25%, on average, based on the work you do and the clients and industries/sectors you serve

Nice to have

  • 1+ years of experience working with structured and/or unstructured data in a cloud or modern data environment such as AWS, Azure, or GCP
  • Exposure to modern data and pipeline tools such as Databricks, Snowflake, BigQuery, dbt, or Airflow
  • Experience with BI and visualization tools such as Power BI, Tableau, or Looker
  • Ability to translate business or user needs into a technical output, workflow, or solution component
  • Exposure to MLOps concepts such as model versioning, experiment tracking, or deployment workflow
  • Participation in hackathons, technical competitions, open-source projects, or portfolio-based technical work
  • Government consulting experience

What the JD emphasized

  • building reliable, maintainable, and well-documented code
  • experience with generative AI tools, LLM APIs, prompt engineering, or RAG-based applications
  • building and deploying GenAI/LLM-powered solutions in client or production environments

Other signals

  • implementation of AI/ML, analytics, and automation solutions
  • prompt engineering, retrieval-augmented generation (RAG) approaches
  • integration of AI/ML models, including generative AI and LLM-based solutions
  • building and deploying GenAI/LLM-powered solutions in client or production environments