As a Senior Data Engineer (AI Analytics Specialist) at Grade 10, you will own the AI data platform strategy for the CIA organization’s India delivery engine. This is an architectural and technical leadership role, not a pipeline-execution role. You will set the standards, make the build-vs-buy calls, and represent the India team as a technical peer to US-based architects and analytics leads.

• Own the end-to-end AI data architecture for the CIA analytics platform—including LLM integration pipelines, prompt-context data layer design, feature engineering infrastructure, RAG architecture, and AI observability frameworks.

• Lead technical decisions on AI tooling choices: Snowflake Cortex vs. external LLM APIs, in-warehouse vs. external inference, vector store selection, embedding pipeline design—with cost, latency, data governance, and maintainability all factored in.

• Design and enforce data quality and observability standards that prevent AI pipeline failures from producing incorrect, hallucinated, or misleading insights at the VP and SVP level.

• Serve as the technical anchor for agentic AI initiatives—evaluating agent frameworks (LangChain, LlamaIndex, custom), designing the data layers that ground agents in structured Cisco CX data, and owning the architectural guardrails that make agents trustworthy in production.

• Mentor and technically guide the junior and mid-level AI data engineers on the India team—owning code review standards, architecture patterns, and upskilling plans for AI readiness.

• Collaborate as a peer with the US-based analytics architecture team and VP-level stakeholders on the 2-year platform roadmap—translating strategic direction into executable architecture, and pushing back when needed.

• Evaluate and prototype AI-native data tooling (Snowflake Cortex, dbt Copilot, Vertex AI, and emerging frameworks) and recommend adoption decisions with production-readiness evidence, not enthusiasm.

Minimum Qualifications

• 10+ years of professional experience in data engineering or analytics engineering, with at least 4 years specifically in AI/ML pipeline development at production scale in an enterprise or high-growth environment.

• Demonstrated ownership of end-to-end AI data systems in production—not prototypes, not pilots. Must be able to describe specific systems, the failure modes encountered, and how they were resolved.

• Expert-level Python for AI pipeline engineering: LLM API integration, multi-turn prompt orchestration, response parsing and structured output validation, retry and fallback logic, and production-grade error handling at API scale.

• Expert-level Snowflake SQL, including deep familiarity with query performance profiling, schema design for AI workloads, and experience evaluating or using Snowflake Cortex for in-warehouse AI operations.

• Demonstrable experience with at least one of: RAG architecture implementation in a structured enterprise data context; agent framework design (LangChain, LlamaIndex, or equivalent); or vector database deployment (Pinecone, Weaviate, pgvector) at production scale.

• Track record of technical mentorship or informal leadership—coaching junior engineers, establishing standards, or driving adoption of a tool or practice across a team without having direct managerial authority.

Preferred Qualifications

• Experience with GCP AI services at an architectural level—Vertex AI pipeline design, Cloud Run for inference serving, BigQuery ML—not just point-tool familiarity, but cross-service integration into a coherent AI data platform.

• Hands-on experience evaluating build-vs-buy decisions on AI tooling in a cost-constrained enterprise environment, including producing documented tradeoff analyses that influenced a senior leadership decision.

• Experience with MLOps or AI pipeline governance practices: model versioning, prompt versioning, output quality monitoring, data drift detection, and structured evaluation frameworks for LLM output quality in production.

• Working knowledge of dbt at an intermediate-to-advanced level—specifically authoring dbt models that produce feature engineering tables or prompt-context data structures for AI consumption.

  • Exposure to Microsoft Fabric, Snowflake Cortex Analyst, or similar AI-integrated analytics infrastructure, with an informed view on how these shift the data engineering role at the platform level.