Sr. AI Native Engineer - Guadalajara, MX

Guadalajara, MX (Hybrid)

- We are seeking high-performing AI-Native Engineers across Senior and Intermediate levels to
- build, operate, maintain, and continuously improve enterprise Generative AI, Agentic AI,
- Multi-Agent systems, and data-centric AI solutions. These are core engineering and production
- operations roles—not research-only or prompt-engineering-only positions.
- In this role, engineers will own and drive the complete lifecycle of enterprise AI implementations
- across Development → Integration → Testing → Deployment → Observability →
- Production Support → Incident Resolution → Optimization → Data Migration →
- Continuous Evaluation. You will be directly responsible for supporting production-grade
- Agentic AI solutions leveraging Gemini, Google Agent Development Kit (ADK), Vertex AI,
- BigQuery, RAG, Vector Databases, GCP, AI observability frameworks, data pipelines, and
- Google-native production infrastructure.

2. Senior vs. Intermediate Level Expectations

Scope & Autonomy Architects complex multi-agent interactions, leads incident resolution,

and owns high-availability architecture and end-to-end continuous evaluation frameworks.

Leadership & Ownership Drives post-incident Root Cause Analysis (RCA), sets CI/CD & safety deployment standards, and leads migration architecture.

Problem Solving Solves complex probabilistic behavior failures, cross-system latency/quota throttling, and data/model drift issues.

- Core Responsibilities
- Agentic AI Engineering

- Develop, deploy, and maintain robust Multi-Agent systems and application workflows.
- Architect and construct supervisor/sub-agent orchestration patterns.
- Implement agent tools, custom extensions, and API integrations.
- Engineer deterministic execution paths surrounding probabilistic LLM reasoning to ensure
- system reliability.
- Design and implement fail-safe mechanisms, retries, and fallback recovery paths.
- Maintain and version agent prompts, system configurations, and schema tool definitions.
- Troubleshoot complex agent execution failures and execution loops.
- Support, optimize, and maintain RAG (Retrieval-Augmented Generation) and vector-search
- pipelines.
- Implement Human-in-the-Loop (HITL)



workflows to enable seamless human intervention and
- verification.
- Support continuous learning frameworks and automated feedback loops

2. Production Maintenance & Operations
- Engineers possess direct, end-to-end responsibility for production system health and 24x7
- resilience. Activities include:
- Conducting daily operational health checks and proactive production monitoring.
- Executing rapid incident triage, log analysis, and Root Cause Analysis (RCA).
- Performing prompt adjustments, configuration changes, bug fixing, and pipeline reruns.
- Investigate and resolve API execution failures, quota limits, throttling, and latency spikes.
- Managing production deployments, shadow launches, and rollback operations.
- Conduct post-incident analysis and implement preventive operational controls.

3. AI Observability & Telemetry
- Build and manage comprehensive observability frameworks for Agentic AI applications,
- monitoring key telemetry such as:
- Agent Operations: Invocation count, latency, task-completion rates, failure rates, fallback
- rates, routing accuracy, and insight acceptance.
- LLM & Tool Performance: LLM call latency, tool/API error rates, token consumption trends,
- and quota consumption.
- Data & Model Quality: Data quality, schema drift, model/extraction drift, prompt regressions,
- and HITL feedback scores.
- Tooling Experience: Hands-on experience with Monarch, PLX, Cloud Monitoring,
- OpenTelemetry, or equivalent enterprise observability systems.

4. Data Migration & Data Engineering
- Participate actively in data migration and modernization initiatives, ensuring data integrity for
- downstream AI behavior:
- Executing legacy data migrations, BigQuery migrations, and schema modernizations.
- Developing ETL/ELT data pipelines, data transformation, validation, and reconciliation
- scripts.



Performing source-to-target quality validation and semantic-layer migrations (LookML, data
- models).
- Managing Vector DB and RAG knowledge-base data migrations and metadata
- synchronizations.
- Implementing enterprise data retention, purging, and archival policies.

5. RAG & Vector Database Operations
- Maintain ingestion and embedding pipelines; execute re-indexing upon source data mutation.
- Validate vector ingestion accuracy, metadata completeness, and freshness of knowledge
- bases.
- Investigate retrieval failures, inaccurate context insertion, and ranking anomalies.
- Support automated RAG regression testing and maintain synchronization between source
- systems, BigQuery, and Vector DBs.

6. Continuous Evaluation & QA
- Establish and maintain Golden Datasets for continuous benchmark testing.
- Execute Shadow Mode testing, Side-by-Side (SxS) output validations, and automated
- prompt evaluations.
- Implement hallucination detection, output drift monitoring, and HITL acceptance analysis.
- Systematically isolate issue origins across: Data → Prompt → Model → Agent Logic → Tool→ Retrieval → Infrastructure → Integration.

7. High Availability, Resilience & Security
- Implement idempotency, timeouts, retry budgets, circuit breakers, dead-letter queues (DLQ),
- and asynchronous processing.
- Manage capacity, autoscaling, and graceful degradation to maintain highly available 24x7
- services.
- Enforce security controls including PII masking/redaction, Identity and Access Management
- (IAM), RBAC/ACL, secret management, audit logging, and automated compliance retention.

4. Technical Qualifications & Stack
- AI & Agentic Frameworks: Gemini, Google ADK (Agent Development Kit), Vertex AI,
- LangChain/LlamaIndex (or equivalent multi-agent frameworks).
- Data & Cloud Infrastructure: GCP (Google Cloud Platform), BigQuery, LookML, Vector
- Databases (Vertex AI Vector Search, Pinecone, Chroma, etc.), ETL/ELT pipelines.
- Observability & Monitoring: Monarch, PLX, Cloud Monitoring, Cloud Logging, Prometheus,
- Grafana, Datadog, or equivalent.
- Software Engineering & CI/CD: Python, REST/gRPC APIs, Docker, Git, CI/CD pipelines,
- automated testing, version control for prompts/configs.

📌 Sr. AI Native Engineer - Guadalajara, MX
🏢 XChange Software
📍 Guadalajara