Job Description
Ovii's Interpretation of the Role
Lead AI/ML Engineer responsible for designing, building, and deploying enterprise‑grade AI agents and LLM‑powered applications. Works cross‑functionally with product, operations, and business teams to turn prototypes into scalable solutions.
Role Snapshot
- Lead AI/ML engineering initiatives
- Design and deploy AI agents
- Build production‑grade LLM applications
- Integrate AI with enterprise platforms
- Create reusable agent frameworks
- Implement observability and guardrails
- Collaborate with technical and non‑technical stakeholders
Must-Have Requirements
- Strong software engineering fundamentals
- Hands‑on experience with LLMs and modern AI application development
- Experience building AI agents or autonomous workflows
- Strong understanding of RAG architectures, prompt engineering, vector databases, tool/function calling, AI workflow orchestration, and context/memory management
- Experience with cloud platforms (Google Cloud, AWS, Azure)
- software engineering
- AI/LLM application development
Nice-to-Have Signals
- Experience with Vertex AI, Gemini Enterprise, OpenAI APIs
- Familiarity with AI evaluation frameworks, observability, and guardrails
- Experience with Google Workspace APIs, Slack integrations
- Knowledge of fine‑tuning, model optimization, or open‑source LLM deployment
- Exposure to fast‑paced startup or innovation environments
- Kubernetes, Docker, CI/CD and cloud‑native deployments
- Contributions to open‑source AI projects
- production deployment of AI applications
- fast‑paced startup environment
Work Setup
- Location: Guadalajara, Mexico
- Work mode: HYBRID
- Employment type: Full-Time
Not Specified in JD
- Visa sponsorship
- Salary range
- Remote eligibility
- Education requirement
- Certifications
- Relocation
- Notice period
- Travel
- Security clearance
- Coding test
- Portfolio
- GitHub
- Writing sample
- Cover letter
What You'll Likely Work On
- Design, build, and deploy AI agents and multi‑agent systems using modern LLM frameworks
- Develop agentic workflows for finance, legal, operations, sales, support, and growth functions
- Create production‑ready applications with RAG pipelines, tool calling, and memory management
- Integrate agents with Google Workspace, Slack, CRM systems, internal APIs, and databases
- Evaluate foundation models (Gemini, OpenAI, Anthropic, open‑source) and select optimal providers
- Build reusable frameworks, prompt libraries, and evaluation pipelines for rapid prototyping
- Implement observability, guardrails, and monitoring to ensure reliable AI deployments
- Optimize performance for latency, accuracy, reliability, and cost
Good Fit If You Have
- Experience in fast‑paced startup or innovation environments
- Familiarity with AI evaluation frameworks and observability tools
- Exposure to vector databases such as Pinecone, Weaviate, or Chroma
- Knowledge of fine‑tuning, model optimization, or open‑source LLM deployment
- Experience integrating with Google Workspace APIs or Slack
Skills
- Python / PySpark
- LLM frameworks (LangChain, LangGraph, CrewAI, etc.)
- Machine learning libraries (TensorFlow, PyTorch, scikit‑learn, Keras, MXNet)
- Statistical analysis (Regression, hypothesis testing, SAS/SPSS)
- Vector databases & RAG pipelines
- Cloud platforms (Google Cloud, AWS, Azure)
- AI orchestration tools (KubeFlow, BentoML)
- Observability & evaluation (Great Expectations, Evidently AI)