Azure/ AWS Agentic AI Architect

Role Summary:

Lead and deliver high-impact initiatives aligned to the AI & Data hiring plan. Own execution excellence with measurable business value, technical depth, and governance.

Key Outcomes (1218 months)

Ship production-grade solutions with clear ROI, reliability (SLOs), and security.

Establish engineering standards, pipelines, and observability for repeatable delivery.

Mentor talent; uplift team capability through reviews, playbooks, and hands-on guidance.

Responsibilities

Translate business problems into well-posed technical specifications and architectures.

Lead design reviews, prototype quickly, and harden solutions for scale (1M+ users / high QPS).

Build automated pipelines (CI/CD) and model/data governance across environments.

Define & track KPIs: accuracy/latency/cost, adoption, and compliance readiness.

Partner with Product, Security, Compliance, and Ops to land safe-by-default systems.

Technical Skills

Data architecture: Lakehouse + medallion (Bronze/Silver/Gold) on Delta Lake

Pipelines: Spark/Databricks, Airflow/Dagster, dbt; streaming with Kafka/Kinesis/PubSub

Storage/Warehouses: ADLS/S3/GCS, Snowflake/BigQuery/Redshift, partitioning/Z-ordering, time travel

Quality & governance: Great Expectations, Deequ, Unity Catalog/Glue; lineage and metadata

Performance: file compaction, AQE in Spark, cost-aware design, caching

Security: IAM, fine-grained access, data masking/row-level security

Architecture & Tooling Stack

Source control & workflow: Git, branching standards, PR reviews, trunk-based delivery.

Containers & orchestration: Docker, Kubernetes, Helm; secrets, configs, RBAC.

Observability: logs, metrics, traces; dashboards with alerting & on-call runbooks.

Data/Model registries: metadata, lineage, versioning; staged promotions.

Performance & Reliability

Define SLAs/SLOs for accuracy, tail latency (p99), throughput, and availability.

Capacity planning with autoscaling; load tests; cache design; graceful degradation.

Cost controls: instance sizing, spot/reserved strategies, storage tiering.

Security & Compliance

IAM, network isolation, encryption (KMS), secret rotation.

Threat modeling, dependency scanning, SBOM, supply-chain security.

Domain-regulatory controls (PCI DSS, HIPAA) where applicable; audit readiness.

Qualifications

Bachelors/Masters in CS/CE/EE/Data Science or equivalent practical experience.

Strong applied programming in Python; familiarity with modern data/ML ecosystems.

Proven track record of shipping and operating systems in production.

Interview Focus Areas

Systems design & trade-offs; failure modes and mitigation.

Hands-on debugging & performance tuning; data quality management.

Security/compliance considerations; stakeholder communication.

Sample Screening Questions

1) Design a low-latency inference service for a fraud model; outline caching, batching, and rollback.

2) Define a strategy to detect drift and trigger retraining with safe deployment.

3) Describe medallion architecture and where data validation should live across layers.