Principal engineer / AI, data, and analytics systems
Akshay Sardana
I build production AI and data systems for complex business workflows.
Most of my work sits between ambiguous business questions and reliable technical systems: conversational analytics, anomaly investigation, commerce automation, privacy-aware data infrastructure, and large-scale data platforms.
Work
Practical systems for AI, analytics, and data-heavy operations
The common thread is reliability: taking work that is ambiguous, manual, or fragile and turning it into systems with clearer data, behavior, and ownership.
Applied AI and Analytics Systems
Production AI and analytical systems that turn ambiguous business, editorial, commerce, and operational signals into governed, reviewable decisions.
- Conversational analytics, anomaly investigation, classification, and defect-detection workflows
- LLM workflows with structured outputs, guardrails, review gates, and observable failure modes
- Retrieval, classification, multimodal verification, and privacy-aware NLP patterns
Data Platform Strategy
Practical architecture and sequencing for fragmented pipelines, warehouse models, and ownership boundaries.
- Architecture and sequencing across BigQuery, dbt, Airflow, streaming, and PySpark
- Data quality, lineage, and model contracts for shared datasets
- Operating model for roadmap, support, and stakeholder intake
LLM Workflow Reliability
Auditable AI workflows with deterministic checks, structured outputs, tool use, human review, and production observability.
- Guardrails for confidence, safety, retries, and failure investigation
- Evaluation harnesses for SQL generation, anomaly narratives, page verification, and classification
- Review gates for multimodal, classification, agentic, and privacy-sensitive systems
Analytics Engineering and Self-Service
Semantic models, dashboard contracts, and enablement practices that let analysts and business teams answer repeat questions with less ad hoc support.
- Reusable dbt models and metrics with tested definitions
- Self-service paths that keep sensitive logic governed
- Training and documentation for engineers, analysts, and operators
Selected Systems
A few representative areas
These are short versions for context. The fuller technical notes keep confidential details omitted but go deeper on constraints, architecture, and outcomes.
Conversational Analytics Agent
A governed natural-language analytics agent that turns business questions into validated BigQuery analysis across Ads, Editorial, Commerce, and operations workflows.
AI Anomaly Analysis and Classification Systems
Applied AI and statistical analysis workflows that classify content, investigate performance anomalies, and surface operational changes with structured outputs, rules, and reviewable evidence.
Shared Data Platform and Self-Service Analytics
A shared analytics foundation for high-volume digital media and commerce data, built to reduce repeated requests and increase safe self-service.
AI Commerce Defect Detection
A reviewable AI workflow that detects commerce catalog and retailer-page defects before operational issues compound.
Approach
Start with the workflow, then make the system reliable
The pattern is direct: understand the decision or user path, ground it in trustworthy data, design for production behavior, and leave teams with maintainable operating practices.
Start with the business workflow
Define the decision, user path, source systems, owners, failure modes, and practical constraints before choosing the AI or data architecture.
Connect AI to governed data
Use source-of-truth datasets, metric contracts, retrieval metadata, and access boundaries so answers can be traced and corrected.
Design for evals and production behavior
Build dry runs, tests, reconciliation checks, confidence gates, observability, and human review into the workflow instead of adding them after launch.
Leave durable systems and team capability
Document decisions, coach internal owners, and leave an operating model that can keep improving after the initial build.
Experience
Hands-on principal engineering across AI, data, and platform work
I usually work where strategy, architecture, implementation, and operating ownership need to meet.
Principal Engineer, AI/ML/Data - Hearst Magazines
Principal Engineer since 2023, promoted from Senior Data Engineer, working inside a large digital media and e-commerce publisher with 140M+ monthly consumers.
- Lead cross-functional data and AI work across roadmap, architecture, stakeholder alignment, and delivery
- Platform impact across multiple engineering, analytics, and business teams
- Build conversational analytics, anomaly analysis, commerce validation, article classification, and shared data-platform systems
Senior Data Engineering - Meta, Point Predictive, 1stDibs, Barclays
Prior work across consumer-scale event pipelines, fintech model infrastructure, e-commerce ML/data systems, and financial technology.
- Worked on real-time NLP PII detection, 5B+ daily event streams, and privacy-aware analytics at Meta
- Improved fintech and e-commerce ML infrastructure through derived data, feature engineering, SageMaker migration, and AWS ML services
- Built across dbt, Airflow, PySpark, Kafka/Kinesis, BigQuery, AWS, GCP, Azure, LangGraph, OpenAI, Anthropic, Bedrock, and Vertex AI
Systems leadership pattern
Hands-on principal engineering work where strategy, architecture, implementation, and operating ownership need to converge.
- Translate ambiguous mandates into scoped delivery plans and measurable system behavior
- Build enough of the critical path to prove the architecture under real constraints
- Create contracts, documentation, and team practices that survive beyond the initial launch
Contact
Continue the conversation
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