Delivery Consultant, Data Engineering and Gen AI Conversion Solutions

This role focuses on developing, designing, and maintaining AI-based systems, with a strong emphasis on Natural Language Processing (NLP) and Large Language Models (LLMs). The consultant will refine prompts, optimize LLM outcomes, and build backlogs for ML-enabled features, contributing to data engineering and conversion solutions for government clients. Experience with Python, NLP/LLM technologies, and cloud platforms is required.

What you'd actually do

  1. Developing, designing, and maintaining cutting-edge AI-based systems, ensuring smooth and engaging user experiences
  2. Participating in a wide variety of Natural Language Processing activities, including refining and optimizing prompts to improve the outcome of Large Language Models (LLMs), and code and design review
  3. Developing and promoting standards across the community
  4. Working with leadership and stakeholders to identify AI opportunities and promote strategy
  5. Building and prioritizing backlog for future machine-learning enabled features to support client business processes

Skills

Required

  • Python
  • R
  • Tensorflow
  • PyTorch
  • Keras
  • NLP
  • LLM
  • GenAI technologies (OpenAI, Claude, Gemini)
  • Machine learning algorithms
  • AWS
  • Azure
  • GCP
  • Big data technologies
  • SQL

Nice to have

  • Government or public sector data programs
  • Master's Degree in related technical discipline
  • Agile delivery methods
  • Agentic AI development

What the JD emphasized

  • 3+ years of experience programming in Python or R with libraries like Tensorflow, PyTorch, or Keras
  • 3+ years of experience with NLP and LLM, especially focused on GenAI technologies such as OpenAI, Claude, Gemini, etc.
  • 1+ years of understanding of machine learning algorithms, including supervised and unsupervised learning

Other signals

  • Developing, designing, and maintaining cutting-edge AI-based systems
  • Participating in a wide variety of Natural Language Processing activities
  • refining and optimizing prompts to improve the outcome of Large Language Models (LLMs)
  • Building and prioritizing backlog for future machine-learning enabled features