Sr. GenAI Engineer

Job Description

Currently implementing vendor tools that provide Guardrails and Observability for Generative AI applications. 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. This person will be responsible for integrating RAG applications with Galileo as well as using guardrail frameworks to suggest the right guardrails for each application.

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.

• Operationalize guardrails and observability across vendor‑based RAG and LLM applications.
• 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 SMLs (e.g., attribution, grounding, prompt injection, tone, PII leakage).
• Implement annotation, structured feedback loops, fine‑tuning, and alignment methods to calibrate judge models.
• Use Lang Chain to orchestrate guardrail checks, manage prompt versioning, and integrate judge model scoring workflows.
• Work with Open Shift 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.
• Ability to translate needs from data stakeholders into solutions.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances.

If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to

• Data Science and AI Engineering background
• -Midlevel: 5 years of professional experience, Sr level: 10 years of professional experience
• Proven hands‑on experience in GenAI Ops — operationalizing LLM and RAG applications in production
• Experience specifically with the OpenAI API, chat completions, embeddings, etc.
• Strong proficiency in Python and data science libraries (Num Py, Pandas, Scikit‑learn, PyTorch/Tensor Flow).
• 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.).
• Design and develop new proof‑of‑concept projects to enhance current AI systems to handle increased traffic and larger datasets with cutting edge.
• Strong hands‑on experience with the Lang Chain framework.
• Hands‑on experience with Open Shift (or Kubernetes) for containerized AI workloads.
• Have a solid awareness on Tensor
  
  RT and VLLM implementation.