GenAI Engineer

About the role

PhoenixTeam is a remote-first organization and applicants for this position can work from anywhere within the US. This role is ideal for someone who enjoys ownership, thrives in ambiguity, and wants to see their work move from prototype to production.

Responsibilities

• Applied AI Engineering & Delivery
  • Design, implement, fine-tune, and deploy generative AI and ML solutions aligned to real business use cases.
  • Translate technical and business requirements into scalable AI architectures and implementation plans.
  • Lead or support end-to-end AI solution delivery, from experimentation through deployment and iteration.
  • Serve as the primary technical voice on GenAI initiatives, ensuring clarity across technical and non-technical stakeholders.
• Model Selection, Tuning & Scaling
  • Evaluate and apply appropriate models (e.g., GPT-based LLMs, BERT-style architectures, or other approaches) based on problem context.
  • Fine-tune, integrate, and operationalize models with support from specialized teammates when needed.
  • Contribute to decisions around model performance, cost, latency, and reliability tradeoffs.
  • Support early MLOps practices, including deployment patterns, monitoring, and iteration.
• Collaboration & Enablement
  • Partner closely with Value Engineers, product leaders, and clients to align AI solutions with business goals.
  • Communicate complex technical concepts clearly to non-technical audiences.
  • Contribute to internal knowledge sharing, patterns, and best practices for GenAI delivery.
  • Mentor teammates and support PhoenixTeam learning and growth initiatives.
• Continuous Learning & Innovation
  • Stay current on emerging GenAI techniques, tools, and platforms.
  • Experiment with new approaches and bring practical insights back to the team.
  • Support PhoenixTeam’s evolution in AI delivery methods, tooling, and ways of working.

Requirements

• Must-have:
  • Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning, or a related field.
  • Experience delivering technical projects that involve collaboration across stakeholders with varying technical depth.
  • Hands-on familiarity with generative AI concepts (e.g., LLMs, fine-tuning, embeddings) and a strong desire to deepen expertise.
  • Experience deploying or working with models on cloud platforms (AWS, Azure, or GCP).
  • Strong communication skills – you can explain why and how, not just what.
  • Ability to take ownership, manage priorities, and deliver outcomes in fast-moving environments.
• Nice-to-have:
  • Exposure to MLOps practices, containerization, or model monitoring.
  • Experience with AI/NLP applications beyond basic experimentation.
  • Familiarity with AI evaluation methods and performance metrics.
  • Interest in ethical AI, governance, and data security principles.
  • Experience in regulated industries (a plus, not a blocker).

Qualifications

• Must-have:
  • Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning, or a related field.
  • Experience delivering technical projects that involve collaboration across stakeholders with varying technical depth.
  • Hands-on familiarity with generative AI concepts (e.g., LLMs, fine-tuning, embeddings) and a strong desire to deepen expertise.
  • Experience deploying or working with models on cloud platforms (AWS, Azure, or GCP).
  • Strong communication skills – you can explain why and how, not just what.
  • Ability to take ownership, manage priorities, and deliver outcomes in fast-moving environments.
• Nice-to-have:
  • Exposure to MLOps practices, containerization, or model monitoring.
  • Experience with AI/NLP applications beyond basic experimentation.
  • Familiarity with AI evaluation methods and performance metrics.
  • Interest in ethical AI, governance, and data security principles.
  • Experience in regulated industries (a plus, not a blocker).