GenAI Architect

Position Overview

We are looking for an enthusiastic GenAI and LLM Architect to add to Credera’s Data capability group. The ideal candidate is excited about leading project-based teams in a client‑facing role to analyze large data sets and derive insights through machine learning (ML) and artificial intelligence (AI) techniques. They have strong experience in data preparation and analysis using a variety of tools and programming techniques, building and implementing models, and creating and running simulations.

The architect should be familiar with the deployment of enterprise‑scale models into a production environment; this includes leveraging full development lifecycle best practices for both cloud and on‑prem solutions across a variety of use cases.

You will act as the primary architect and technical lead on projects to scope and estimate work streams, architect and model technical solutions to meet business requirements, and serve as a technical expert in client communications. On a typical day you may participate in design sessions, provision environments, and coach and lead junior resources on projects.

• Proven experience in the architecture, design, and implementation of large scale and enterprise grade AI/ML solutions
• 5+ years of hands‑on statistical modeling and/or analytical experience in an industry or consulting setting
• Master’s degree in statistics, mathematics, computer science or related field (a PhD is preferred)
• Experience with a variety of ML and AI techniques (e.g. multivariate/logistic regression models, cluster analysis, predictive modeling, neural networks, deep learning, pricing models, decision trees, ensemble methods, etc.)
• Proficiency in programming languages such as Python, Tensor Flow, PyTorch, or Hugging Face Transformers for model development and experimentation
• Strong understanding of NLP fundamentals, including tokenization, word embeddings, language modeling, sequence labeling, and text generation
• Experience with data processing using Lang Chain, data embedding using LLMs, vector databases and prompt engineering
• Advanced knowledge of relational and non‑relational databases (SQL, No
  
  SQL)
• Proficient in large‑scale distributed systems (Hadoop, Spark, etc.)
• Experience with designing and presenting compelling insights using visualization tools (RShiny, R, Python, Tableau, Power
  
  BI, D3.js, etc.)
• Passion for leading teams and providing both formal and informal mentorship
• Experience with wrangling, exploring, transforming, and analyzing datasets of varying size and complexity
• Knowledgeable of tools and processes to monitor model performance and data quality, including model tuning experience
• Strong communication and interpersonal skills, and the ability to engage customers at a business level in addition to a technical level
• Stay current with AI/ML trends and research; be a thought leader in AI area
• Experience with implementing machine learning models in production environments through one or more cloud platforms:
  • Google Cloud Platform
  • Azure cloud services
  • AWS cloud services

• Thrive in a fast‑paced, dynamic, client‑facing role where delivering solid work products to exceed high expectations is a measure of success
• Contribute in a team‑oriented environment
• Prioritize multiple tasks in order to consistently meet deadlines
• Creatively solve problems in an analytical environment
• Adapt to new environments, people, technologies and processes
• Excel in leadership, communication, and interpersonal skills
• Establish strong work relationships with clients and team members
• Generate ideas and understand different points of view

Denver Pay Range: $130,000 — $170,000 USD

Hybrid Working Model:
Employees have the flexibility to work remotely two days a week. Team members are expected to spend three days in person, with the freedom to choose the days and times that best suit their projects and teams.

For consulting roles, the goal is to minimize travel. Most projects do not require extensive travel. While some projects may involve up to 80% travel for a period, the annual average for team members is typically 10%–30%. Travel preferences are considered…