Delivery Senior Consultant, Data Engineering and Gen AI

Senior Consultant, Data Engineering role focused on designing, building, and executing data engineering and conversion solutions for government clients, with a specific emphasis on AI-based systems, NLP, and LLMs. The role involves refining prompts, optimizing LLM outcomes, and building ML-enabled features.

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
  • OpenAI
  • Claude
  • Gemini
  • Machine learning algorithms
  • Supervised learning
  • Unsupervised learning
  • AWS
  • Azure
  • GCP
  • Big data technologies
  • Angular
  • React
  • NodeJS
  • C#
  • .Net
  • Java
  • Golang
  • SQL

Nice to have

  • Agentic AI development

What the JD emphasized

  • 6+ years of experience programming in Python or R with libraries like Tensorflow, PyTorch, or Keras
  • 5+ years of experience with NLP and LLM, especially focused on GenAI technologies such as OpenAI, Claude, Gemini, etc.
  • 2+ years of understanding of machine learning algorithms, including supervised and unsupervised learning
  • Proven experience with with cloud-hyperscalers (AWS, Azure, GCP)
  • Proven experience with big data technologies, i.e Angular, React, NodeJS, C#, .Net, Java, Golang, SQL

Other signals

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