Vishwajeet Thakur

I’m a backend, data, and GenAI engineer who enjoys building systems that are practical, scalable, and actually useful to the teams depending on them. Over the past few years, I’ve worked across cloud platforms, data pipelines, APIs, and AI-powered applications, with most of my work centered on Python, AWS, FastAPI, Spark, Snowflake, and retrieval-based GenAI systems.

At Consultadd, I’ve worked in client-facing engineering roles for organizations like Pfizer, Charter Communications, and Charles Schwab. That experience taught me how to balance clean technical design with real business needs. I’ve built production FastAPI services, designed persistence layers with SQLAlchemy and PostgreSQL, developed Spark and Airflow-based ETL pipelines and delivered cloud-native systems on AWS using EKS, S3, RDS, and Docker.

A big part of my recent work has been in GenAI and platform engineering. I’ve built RAG systems for log analytics, runbook search, and investigation workflows using LangChain, LangGraph, Pinecone, Bedrock, and OpenAI embeddings. I care a lot about making these systems reliable in production, so I’ve also focused on observability, structured outputs, evaluation, testing, and MLOps tooling like ClearML and LangSmith.

What I bring is a mix of engineering depth and product thinking. I’m comfortable going deep into backend architecture, data modeling, and distributed pipelines, but I also pay attention to performance, maintainability, and the end-user experience. Whether it’s reducing ingestion time from minutes to seconds, building reusable internal platforms, or shipping AI features with proper guardrails, I like solving problems in a way that is thoughtful, measurable, and production-ready.