Generative AI Engineer

TEKsystems•Charlotte, NC

1d•$65 - $85•Hybrid

About The Position

We are seeking a GenAI Engineer with proven expertise in GenAI Ops — operationalizing, monitoring, and scaling LLM and RAG-powered applications with robust guardrails and observability. The role will focus on leveraging LLMs as Judges and Specialized Models (SMLs) to measure and score guardrail metrics such as chunk attribution, context adherence, prompt injection detection, tone, sexism, bias, and PII leakage. Success requires strong skills in annotation, fine-tuning, and alignment techniques to calibrate these judge models, and in bringing all of this into an operational framework for enterprise readiness. Data Science and AI Engineering skillset required for enablement of AI Technology Strategy. Data Architecture Strategy Lead performs flawless, end to end execution of cross functional, high impact strategic data related initiatives and/or large programs that have significant influence on how the company manages data. Execution plans outline multi-year strategic outcomes (based on target state AI Technology products and tools) and the activities required to support achievement of those outcomes. Communicates, influences and negotiates both vertically and horizontally to obtain or leverage necessary resources. Knowledgeable in the agile framework, demonstrate a strong combination of strategic thinking, tactical planning and project management skills along with the ability to lead and influence project teams without direct management.

Requirements

Bachelor’s or master’s degree in Data Science, Computer Science, MIS, related field, or equivalent experience
Proven hands-on experience in GenAI Ops — implementing LLM and RAG applications in production.
Strong hands-on experience with the LangChain framework
Experience specifically with the OpenAI API, chat completions, embeddings, etc.
Have a solid awareness on TensorRT and VLLM implementation
Strong proficiency in Python and data science libraries (NumPy, Pandas, Scikit-learn, PyTorch/TensorFlow).
Proven experience applying guardrails and observability to LLM or RAG-powered applications.
Experience with LLMs as Judges and SMLs for evaluation (attribution, adherence, bias, PII, etc.).
Hands-on experience with OpenShift (or Kubernetes) for containerized AI workloads.
Experience measuring and optimizing inference latency.
Strategic and innovative thinker, able to solve complex problems and develop solutions incorporating data/research to support recommendations and risk/reward tradeoffs
Ability to lead and drive change and deliver results in a heavily matrixed environment
Demonstrated ability to influence a wide variety of stakeholders including executives
Self-starter with an exceptional drive for results and success; conveys a sense of urgency to achieve outcomes and exceed expectations; persists despite obstacles, setbacks and competing influences
Outstanding communication and presentation skills (verbal and written) across all management levels. Must be able to concisely summarize key observations, clearly articulate considerations, propose solutions
Solution design - demonstrates expertise in solution design across multiple technologies; can identify opportunities for technical training and coaching across the organization.
Demonstrated expertise in AI strategies and tools
Can identify opportunities for technical training and coaching across the organization.
10+ years of experience designing and developing AI solution architectures to scale
Design and develop new proof of concept projects to enhance current AI systems to handle increased traffic and larger datasets with cutting edge.
Experience in Agile frameworks and Agile at scale.
Ability to provide input for financial planning, tracking, and managing budget variances.

Responsibilities

Implement guardrails and observability for various RAG and LLM applications across enterprise. This includes application like content summarizer, conversational and etc..
Set up GenAI Ops workflows to continuously monitor inference latency, throughput, quality, and safety metrics.
Define, track, and analyze RAG guardrail metrics using LLMs as Judges and SLMs(e.g., attribution, grounding, prompt injection, tone, PII leakage).
Implement annotation, structured feedback loops, fine-tuning, and alignment methods to calibrate judge models.
Use LangChain to orchestrate guardrail checks, manage prompt versioning, and integrate judge model scoring workflows.
Work with OpenShift to deploy, scale, and monitor containerized GenAI services.
Build observability dashboards and alerts (Grafana or equivalent) for AI reliability.
Contribute to emerging agentic evaluation and guardrails as autonomous AI workflows expand.
Ability to lead cross-functional work and motivate cross-functional teams to achieve business objectives and process improvements