Application Development Technical Lead - Semantic & AI Agent Engineer (Data Services)

Build Semantic Data Agents: Design, develop, test, and operate production-grade Semantic Data Agents using Google ADK, LangChain, LangGraph (or equivalent agentic frameworks). Engineer agents that encapsulate governed business meaning, analytical logic, metric definitions, policies, and domain semantics. Ensure all agent outputs are grounded in authoritative systems of record and governed semantic definitions. No unverified, non-traceable, or low-confidence information may be released to downstream agents, users, or systems without explicit Human-in-the-Loop (HIL) approval.

Own the Semantic Data Layer: Translate complex business data concepts into reusable semantic assets, including certified metrics, KPIs, dimensions, hierarchies, calculation logic, thresholds, business rules, decision policies, and contextual definitions. Establish a single source of truth for business meaning that can be consistently consumed across agentic, analytical, and reporting workloads.

Verification, Provenance & Trust Controls: Implement verification-by-default architectures incorporating source grounding, data lineage, confidence scoring, explainability, policy validation, and auditability. Ensure agents provide transparent reasoning, traceable evidence, and verifiable references for every business-critical conclusion, recommendation, calculation, or alert.

MCP Tooling & Semantic Service Exposure: Consume and build MCP tools that expose governed business metrics, semantic knowledge, analytical reasoning, and decision-support capabilities. Design MCP interfaces that abstract underlying data complexity and provide trusted, business-aligned access patterns for downstream agents.

Governance & Enterprise Readiness: Ensure semantic services can operate reliably in regulated environments requiring compliance, traceability, and reproducible outcomes.

Agile Tech Leadership: Act as the Technical Lead for the Semantic Layer Squad , taking hands-on responsibility for sprint planning, daily stand-ups, backlog grooming, and sprint execution.

Code Quality & Reviews: Establish rigorous code review processes within your squad, ensuring adherence to coding standards, comprehensive unit testing (minimum 80% coverage), and robust security practices.

Developer Mentorship: Mentor and guide junior and mid-level software developers within your squad, fostering an "AI-First" engineering culture and driving the adoption of generative AI coding assistants.

Distribution Squad Collaboration: Work in close partnership with the Distribution Squad to consume their exposed data virtualization products and raw APIs, negotiate data contracts, and align schemas.

AI Architecture Alignment: Partner closely with the wider department's AI design and Enterprise Architecture groups to ensure all conversational and agentic AI deliverables align with global enterprise standards and platform runtimes.

Telemetry & Observability: Implement comprehensive logging, tracing, and metric-forwarding (using tools like Splunk, Prometheus, Grafana, or AppDynamics) within your agents and MCP servers to ensure ease of production estate management.

Tech Debt Prevention: Proactively drive clean, modular, and well-documented codebases that prevent the incurrence of technical debt across the application lifecycle.

Security & Compliance: Ensure all agentic workflows and tool executions strictly adhere to enterprise security guidelines, including secure token handling ( OAuth 2.0 Machine-to-Machine / M2M ), row/column-level masking (Apache Ranger), and secure network policies.

Source Grounding & Fact Verification Validation: Establish automated validation and evaluation frameworks (using tools like Ragas, TruLens, or custom assertion suites) to continuously test and verify that agent responses are strictly grounded in virtualized data products, eliminating hallucinations.

Confidence & Explainability Benchmarking: Design and execute benchmarking strategies to validate confidence scoring models and ensure that the agent's reasoning paths and tool-execution logs are transparent, explainable, and easily interpretable by downstream systems.

Lineage & Auditability Verification: Validate end-to-end data lineage and provenance tracking for every data point retrieved and decision made by the agents, ensuring the entire reasoning process is fully auditable and compliant with enterprise risk standards.

Human-in-the-Loop (HIL) & Guardrails Enforcement: Implement and test robust guardrails (such as prompt injection defenses, PII sanitization, and output validation) and design seamless HIL handoff workflows for high-risk analytical decisions.

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