AI Engineer
Tential Solutions · United States
- Hybrid
- Contract
- $150,000 / year
- United States
Tailored resume — keyword-matched to this role.
Hiring manager — we find who's hiring.
Intro email — drafted to reach them directly.
Job highlights
- Build enterprise-scale GenAI products.
- Work on frontier AI, graph science, agentic systems.
- Requires deep systems-level AI understanding.
- Hands-on experience with advanced AI methods.
- Production-grade AI system development required.
About the role
AI Engineer / Scientist — GenAI Practice
Client: Leading Big 4 Consulting Firm
Position Type: Contract
Location: Remote
Clearance/Compliance: US Citizenship or Green Card Required (No Visa Sponsorship Available)
About The Team
We are sourcing elite engineering and scientific talent for the dedicated GenAI Practice of a leading Big 4 consulting firm. This team is embedded across multiple lines of service, building and deploying enterprise-scale, AI-powered products at the absolute frontier of generative AI, graph science, and agentic systems.
This is a hands-on, highly technical environment. We are bypassing surface-level prompt engineering to build sophisticated, production-grade systems deeply rooted in foundational methods and core AI architecture.
What We Are Looking For
We Want Hands-on AI Engineers And Scientists Who Possess An In-depth, Systems-level Understanding Of Advanced AI Methods. Candidates Must Have Deep Technical Expertise Across One Or More Of The Following Core Frontier Domains:
- Graph Data Science & GNNs: Expertise in Graph Neural Networks (GNNs), graph data science, knowledge graphs, ontology, semantics, and graph-based reasoning or representation learning.
- Agentic AI: Demonstrated experience in agentic system design, autonomous workflow orchestration, agent-to-agent communication frameworks, and multi-agent architectures.
- NLP & LLM Infrastructure: Deep knowledge of large language model architecture, custom fine-tuning, quantization, model evaluation, and multi-modal or retrieval-augmented generation (RAG) pipelines.
- Machine Learning & Deep Learning: Hands-on experience with ML/DL model development and deployment at scale, including GPU computing, cluster infrastructure, and hardware optimization.
- Generative Systems: Advanced familiarity with GANs, VAEs, and diffusion-based generative architectures.
Education & Experience
- MS or PhD in Computer Science, Artificial Intelligence, Data Science, or a highly quantitative field is strongly preferred.
- Production Experience: A proven track record of building, optimizing, and shipping production-grade AI systems at scale (not just localized scripts or theoretical models).
- Systems-Level Depth: In-depth knowledge of underlying systems, algorithms, and frameworks—candidates must understand how the models function under the hood.
Key skills/competency
- Generative AI
- Graph Neural Networks (GNNs)
- Agentic AI
- Large Language Models (LLMs)
- NLP
- Machine Learning
- Deep Learning
- Production AI Systems
- AI Architecture
- Consulting
Skills & topics
- AI Engineer
- GenAI
- Generative AI
- Graph Neural Networks
- GNNs
- Agentic AI
- LLM
- Large Language Models
- Machine Learning
- Deep Learning
- NLP
- Artificial Intelligence
- Data Science
- Computer Science
- Consulting
How to get hired
- Tailor your resume: Highlight your experience in Graph Data Science, Agentic AI, LLM Infrastructure, and ML/DL. Emphasize production-grade systems and systems-level depth.
- Showcase your portfolio: Prepare to discuss your contributions to large-scale AI projects, demonstrating your ability to build and deploy complex systems.
- Understand the client: Research the client's standing as a 'Big 4 Consulting Firm' and their GenAI practice to align your discussion points with their goals.
- Prepare for technical deep dives: Expect in-depth questions about foundational AI methods, algorithms, and frameworks.
Technical preparation
Master GNNs and graph data science fundamentals.,Practice agentic system design and orchestration.,Deepen LLM architecture and fine-tuning knowledge.,Build and deploy production ML/DL systems.
Behavioral questions
Describe a complex AI system you built.,How do you ensure AI system scalability?,Explain your approach to AI model debugging.,How do you stay current with AI advancements?
Frequently asked questions
- What specific AI domains are most critical for the AI Engineer GenAI Practice role at Tential Solutions?
- The AI Engineer GenAI Practice role prioritizes expertise in Graph Data Science & GNNs, Agentic AI, NLP & LLM Infrastructure, Machine Learning & Deep Learning, and Generative Systems. Demonstrating deep technical knowledge and production experience in one or more of these areas is key.
- What is the expected educational background for the AI Engineer GenAI Practice position?
- While not strictly mandatory, an MS or PhD in Computer Science, Artificial Intelligence, Data Science, or a closely related quantitative field is strongly preferred for the AI Engineer GenAI Practice role at Tential Solutions.
- Does Tential Solutions offer visa sponsorship for the AI Engineer GenAI Practice role?
- No, Tential Solutions does not offer visa sponsorship for this AI Engineer GenAI Practice position. US Citizenship or a Green Card is required.
- What distinguishes this AI Engineer role from a typical prompt engineering position?
- This AI Engineer role bypasses surface-level prompt engineering. It focuses on building sophisticated, production-grade AI systems rooted in foundational methods and core AI architecture, requiring a deep, systems-level understanding of advanced AI.
- What does 'Systems-Level Depth' mean for the AI Engineer GenAI Practice role?
- Systems-Level Depth for this AI Engineer role means having an in-depth knowledge of the underlying systems, algorithms, and frameworks. Candidates must understand how AI models function internally, not just how to use them.