Highlights
What You Will Do: Advise on architecture decisions for AI use cases involving SLM, LLM, hybrid AI pipelines across multiple AI tasks like classification, information extraction, document processing, correlation, and reasoning workloads. You will help us make the right decisions on model architecture, tooling, implementation sequencing, and team structure, with a specific focus on when to use SLMs vs LLMs and how to build cost-efficient, production-grade AI pipelines.
Numbers & Facts
| Location | Frisco |
| Industry | Computer/IT Services |
| Company Size | 100 to 499 employees |
| Year Founded | 2009 |
| Website | http://www.tekwissen.com/ |
Description
Overview:
TekWissen is a global workforce management provider headquartered in Ann Arbor, Michigan that offers strategic talent solutions to our clients world-wide. Our client provider of digital technology and transformation, information technology and services
Position:Senior AI Consultant
Location:Frisco TX / Bellevue WA
Duration: 7 Months
Job Type: Temporary Assignment
Work Type:Onsite
Job Description:
About the Role:
TekWissen® Group is an equal opportunity employer supporting workforce diversity.
TekWissen is a global workforce management provider headquartered in Ann Arbor, Michigan that offers strategic talent solutions to our clients world-wide. Our client provider of digital technology and transformation, information technology and services
Position:Senior AI Consultant
Location:Frisco TX / Bellevue WA
Duration: 7 Months
Job Type: Temporary Assignment
Work Type:Onsite
Job Description:
About the Role:
- We are looking for a Senior AI Consultant to serve as a strategic advisor and technical architect for our AI transformation programme.
- The engagement spans multiple high-impact use cases in Telco Ops, along with a broader model selection and cost-governance framework.
- You will play a thought leadership role, guiding senior stakeholders on AI strategy, architecture decisions, and execution models-bringing both hands-on expertise in GenAI and traditional AI/ML as well as experience advising VP/Sr. Director-level leadership in large enterprises.
- You will help us make the right decisions on model architecture, tooling, implementation sequencing, and team structure, with a specific focus on when to use SLMs vs LLMs and how to build cost-efficient, production-grade AI pipelines.
- Advise on architecture decisions for AI use cases involving SLM, LLM, hybrid AI pipelines across multiple AI tasks like classification, information extraction, document processing, correlation, and reasoning workloads.
- Review and challenge model selection choices, benchmarking methodology, and fine-tuning strategies for different AI tasks tasks
- Guide the cost-versus-accuracy trade-off analysis across model types (frontier LLM, LLM with fine-tuning, SLM instruct, SLM fine-tuned) and workload profiles.
- Provide practical input on implementation approach, team structure, sprint sequencing, and make-vs-buy decisions.
- Review data strategy, labelling effort sizing, evaluation harness design, and MLOps requirements for each workload.
- Advise on how to structure the business case and design the appropriate AI architecture including executive-level cost, latency, and accuracy comparisons.
- Flag risks including vendor lock-in, model drift, data governance gaps, and compliance requirements for use cases in regulated industries/domains
- Act as a trusted advisor to senior leadership (VP/Sr. Director level), shaping AI strategy and influencing key decision-making forums.
- 8+ years of experience in applied ML and AI, with at least 3-4 years in enterprise NLP or LLM/SLM system design and deployment.
- Demonstrable hands-on experience with SLMs including fine-tuning and deployment using models such as Phi, Gemma, Llama, Mistral, or Qwen families.
- Strong understanding of frontier LLM APIs (OpenAI, Azure OpenAI, Anthropic) and when they add genuine value over smaller models.
- Experience designing multi-task NLP pipelines covering classification, named entity recognition, document extraction, RAG, and reasoning.
- Ability to translate model architecture decisions into cost models and business cases (implementation cost, run cost, savings, ROI).
- Experience with at least one of the following verticals: telecom, healthcare, or industrial/manufacturing B2B operations.
- Experience with automation or workflow orchestration in high-volume operational environments.
- Knowledge of LLMOps practices for SLM deployment including quantization, batching, model versioning, and latency benchmarking.
- Clear, defensible architecture recommendation for each use case with rationale for model tier selection, estimated implementation cost, and projected run cost savings.
- A practical evaluation framework and scoring rubric that the internal team can use to benchmark models independently.
- A sequenced implementation roadmap that the delivery team can execute in 4-6 month phases.
- Executive-ready cost comparison across LLM-only, SLM-only, and hybrid approaches for each use case.
TekWissen® Group is an equal opportunity employer supporting workforce diversity.
About Company
WE THE TEKWISSEN PEOPLE
TekWissen offers you a broader portfolio of services, industry-leading solutions, and the meaningful innovations that give you greater flexibility and speed to respond to market dynamics, reduced costs and risk to improve enterprise performance, and increased productivity to enable growth.
To keep pace with global market demands, TekWissen keeps its finger on the pulse of change. Our organized approach to guiding a project from its inception to closure. Managing projects is becoming more and more important as we enter the digital era. To cope with the pace that this transition demands, a method is required to manage projects so they can yield quality work, while incorporating efficient use of time and resources.
Project involves identifying which quality standards are relevant to the project and determining how to satisfy them.
It is important to perform quality planning during the Planning Process and should be done alongside the other project planning processes because changes in the quality will likely require changes in the other planning processes, or the desired product quality may require a detailed risk analysis of an identified problem. It is important to remember that quality should be planned, designed, then built in, not added on after the fact.
Capabilities and accomplishments in one TekWissen business enhance the opportunity for success in the others. Put simply, TekWissen's unique combination of attributes promotes success.