Job Description
Senior AI Engineer Appex Innovation - 4.0 Frisco, TX Job Details 1 day ago Qualifications AI models Financial model development System design Classification (ML) AI platforms (beyond public GPTs) System design for system development Classification analysis Machine learning cloud services Model deployment Cost analysis NER Developing large-scale AI models Natural language processing APIs Cost reduction analysis Cost estimates Model training System deployment Cost-benefit analysis (CBA) Design (software development lifecycle) Generative AI Data extraction Full Job Description We are hiring a Senior AI Engineer for our partner in Frisco, TX or Bellevue, WA for an onsite role.
Job Details:
Role:
Senior AI Consultant Location:
Frisco TX or Bellevue WA - Onsite Mandatory Areas:
- AI,ML NLP LLM/SLM RAG
About the Role We are looking for a Senior AI Consultant to serve as a strategic advisor and technical architect for our AI transformation program. 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. 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. 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. What You Must Have 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, Gamma, 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. What is highly desirable 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. What success looks like in this role 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.
