AI Solution and Platforms Junior Architect/Engineer

Overview

The Junior AI Observability Architect is an execution-focused engineer who designs, builds, and operates observability capabilities within a defined domain of the enterprise AI observability platform. Working under the strategic direction of the Senior AI Observability Architect, this role translates architecture blueprints into production-grade instrumentation, telemetry pipelines, dashboards, quality gates, and safety signals across agentic AI systems.

The junior architect is a hands-on engineer who codes, integrates, tests, and iterates owning feature-level delivery within one or more specialization tracks while developing a growing understanding of the full observability platform. They are a technical practitioner first, with an emerging architect mindset.

Responsibilities

1. Observability Platform Engineering OTEL Integration (25%)

• Implement OpenTelemetry (OTEL) instrumentation within assigned agent frameworks or platforms including custom exporters, span enrichers, semantic conventions, and context propagation hooks.
• Build and maintain telemetry pipeline components (collectors, processors, exporters) that route metrics, logs, traces, and semantic signals to central observability backends.
• Integrate OTEL with enterprise agentic platforms as assigned which may include Salesforce AgentForce, ServiceNow, Microsoft Agent 365, or internal frameworks following architecture blueprints set by the L11.
• Develop and maintain observability dashboards, alerting rules, and SLO/SLA definitions for the assigned sub-domain, ensuring signal quality and low false-positive rates.
• Participate in on-call rotations and incident response for the observability platform contributing to RCA documentation and runbook improvement.
• Write unit, integration, and end-to-end tests for all telemetry components; maintain >80% test coverage across owned services.

2. Safety, Security Red Teaming Support (15%)

• Instrument safety-critical signal capture within assigned pipelines including guardrail trigger rates, policy violation events, prompt injection detections, and hallucination flags.
• Support red team exercises by building observability hooks that capture adversarial test results, attack surface telemetry, and behavioral deviation signals in real time.
• Implement secure trace handling for sensitive AI decision events applying data masking, PII redaction, and audit-log retention policies as defined by the security architecture.
• Assist in maintaining the Security Observability Playbook documenting findings, updating escalation paths, and contributing to incident classification procedures.
• Monitor agent-to-agent protocol traffic (A2A, UCP, AP2) for anomalous communication patterns and flag deviations for review by the L11 architect and security team.

3. Responsible AI (RAI) Governance Signal Instrumentation (10%)

• Implement RAI signal collectors within assigned agent workflows capturing fairness indicators, bias detection outputs, explainability scores, and content safety classifications.
• Maintain RAI telemetry pipelines and ensure data quality, completeness, and timeliness of governance signals feeding into compliance dashboards.
• Contribute to audit-readiness work by ensuring all AI decision traces within the assigned domain include required governance metadata and are retained per policy.
• Support gap analyses by comparing current RAI signal coverage against governance framework requirements and flagging coverage gaps to the L11.

4. Quality Engineering for Agentic Solutions Post Go-Live Continuous QE (15%)

• Build and maintain quality gate components within CI/CD pipelines using observability data to detect performance regressions, behavioral drift, and SLA breaches before they reach production.
• Instrument and monitor Skill Evaluations (evals) across the Memory, Skills, and MCP harness stack collecting eval results, tracking pass/fail trends, and alerting on regression thresholds.
• Implement continuous quality monitoring for post-go-live agentic solutions tracking agent success rate, tool-call fidelity, latency distributions, and user outcome proxies.
• Conduct structured testing of new agent capabilities using standardized eval harnesses documenting results and feeding findings into quality improvement cycles.
• Develop automated quality reports and quality metric dashboards for stakeholder review, surfacing trends and anomalies in agent behavior over time.

5. Memory, Skills, MCP Harness Engineering Observability (10%)

• Instrument agent memory operations (read/write latency, cache hit rates, memory drift) across episodic, semantic, and working memory backends within the assigned scope.
• Add trace instrumentation to MCP server interactions tagging tool registrations, skill invocations, context injections, and result returns with semantic OTEL attributes.
• Capture harness execution telemetry for self-evolving and RL systems logging reward signals, policy update events, environment transitions, and convergence indicators.
• Monitor skill eval harness execution pipelines detecting flaky evals, environment setup failures, and result inconsistencies that could mask real capability regressions.

6. Data Science Python Engineering (10%)

• Write production-grade Python for observability tooling custom OTEL exporters, signal aggregators, anomaly detectors, and data transformation pipelines adhering to team engineering standards.
• Apply basic statistical and data science methods to telemetry data time-series analysis, threshold tuning, distribution characterization to improve signal quality and alerting precision.
• Contribute to Python SDK and library development that simplifies OTEL onboarding for agent developers across the organization.
• Participate in code reviews, apply test-driven development practices, and continuously improve the quality and maintainability of the observability codebase.

7. Agent Fleet, Physical AI Multi-Modal Observability (5%)

• Implement telemetry for agent fleet coordination capturing spawn/termination events, inter-agent communication traces, load distribution metrics, and fleet health indicators.
• Contribute to observability instrumentation for physical AI pipelines (edge inference, sensor fusion, robotics control loops) as directed focusing on latency, reliability, and data quality signals.
• Add OTEL instrumentation to multi-modal model pipelines tracing vision, audio, and text input processing stages and capturing cross-modal alignment quality signals.

8. Agentic Marketplace, Registry A2A / UCP / AP2 Observability (5%)

• Instrument the Agentic Marketplace and Agent Registry with usage telemetry tracking agent invocations, capability health scores, adoption trends, and dependency relationships.
• Implement protocol-level observability for A2A (Agent-to-Agent), UCP, and AP2 communication flows capturing message latency, error rates, retry patterns, and trust boundary crossings.
• Contribute to Marketplace Observability Dashboard