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AI-Focused Engineering View

This view emphasizes AI product engineering where language models sit inside governed systems: multi-LLM orchestration, graph subflows, metadata grounding, prompt tracing, query execution, answer reasoning, and chart generation.

Switch views to reorder the same experience around backend, platform, data, or AI roles. Current content refreshed May 2026.

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Backend platform Service ownership, data APIs, and production reliability. Data platform Pipelines, mesh foundations, streaming, and governance. AI data products Governed agents, text-to-data, visualization, and Python.

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Overall profile

Projects

Relevant projects

Governed conversational data platform

I architected and built a governed conversational data and visualization agent: it retrieves business knowledge, answers business questions, runs governed queries from that context, reasons over results, and builds charts without making the LLM the data boundary.

Python FastAPI LangGraph Langfuse

Moved analytics discovery toward self-service by connecting a multi-LLM graph runtime with knowledge retrieval, governed Trino execution, answer reasoning, chart generation, prompt tracing, and a richer Open WebUI experience. The natural-language analytics path reduced query time by about 40% for supported workflows.

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CI/CD onboarding and developer-experience framework

Created an in-house YAML-driven CI/CD framework that let teams onboard projects with very little friction while keeping validation, security scans, deployment behavior, and Jira status updates standardized.

In-house CI/CD YAML Jenkins Docker Kubernetes (OCP)

Improved developer experience, reduced CI/CD onboarding time by about 40%, and increased CI/CD adoption several-fold by replacing bespoke Jenkins pipelines with a small project-level YAML file, reusable templates, automated quality gates, and Jira-triggered delivery flows.

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Browsing-log analytics and safe-browsing pipelines

Built browsing-log ingestion and analytics pipelines for safe-browsing classification, audience management, cohort creation, and pattern-based downstream data products.

NiFi Spark Airflow Python

Turned raw browsing activity into governed analytical signals: threat classification, spam URL marking, audience segments, browsing-pattern cohorts, and reusable data products.

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Retail adjacency and store-flow analytics

Built reusable analytics workflows for cross-shopping, category adjacency, aisle-flow, and store-flow analysis across departments, categories, and products.

Spark Python Power BI Airflow

Helped retail teams move from isolated category views to placement and assortment decisions associated with up to 20% sales-growth impact.

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Experience

Relevant experience

Data Platform Engineer / Architect · Airtel Digital

I architect and build governed data platforms, metadata services, workflow orchestration, access-governance integrations, in-house CI/CD onboarding, and network-scale analytics systems.

2021 - present

  • Architected and built a governed conversational data platform for text-to-data workflows: multi-LLM subgraphs, knowledge retrieval, controlled query execution, answer reasoning, chart generation, prompt tracing, and platform orchestration.
  • Built mobile tower network-event analytics, browsing-log data products, and real-time point-of-interest proximity pipelines, including petabyte-scale aggregation, safe-browsing classification, audience cohorts, and location-triggered downstream actions.
  • Created an in-house YAML-driven CI/CD onboarding framework that reduced onboarding time by about 40%, with Jenkins runner and Docker-agent build modes, mandatory pre-commit checks, unit-test reporting, security scans, deployment templates, and Jira-triggered workflow/status integration.

Data Science Engineer · dunnhumby

I worked between data science and big-data platform teams, turning statistical and ML analysis into reusable pipelines, data marts, reporting products, and client-ready analytics workflows.

2018 - 2021

  • Worked as a bridge between data science and big data platform teams by turning analytical work into reusable products and repeatable pipelines.
  • Built customer segmentation, data marts, reporting platforms, and retail optimization analytics for large datasets, including category/adjacency work associated with up to 20% sales-growth impact.
  • Created reusable retail analytics workflows across customer priority assortment, cross-shopping behavior, direct-mail promotion planning, category uplift, and seasonality analysis.

Software Engineer · Mphasis

I worked on mainframe and big-data systems for insurance and telecom clients, including Spark migration work and automation that saved 500+ manual hours per year.

2014 - 2018

  • Built automations that saved 500+ manual hours per year by removing repetitive workflow steps.
  • Worked in both mainframe and big data ecosystems and contributed to migration from mainframe workloads to Spark-based processing.
  • Worked as a developer for insurance and telecom clients across enterprise systems and data workflows.

Skill stack

Primary skills

Python LangGraph LLM Workflows Vector Search FastAPI Langfuse Open WebUI Postgres

Supporting skills

Spark JavaScript SQL Airflow Next.js Power BI

Additional skills

Flink Java Apriori Kubernetes (OCP) Kafka Cloud (GCP, AWS, Azure)