AI Engineer
1POINT1 · Pune, India
2025.09 – Present · 10 mos
Building production AI systems including NL-to-SQL platforms, document intelligence workflows, and enterprise automation pipelines. I mainly work with RAG, FastAPI, PostgreSQL, LangChain to ship useful AI products and workflow automation.
AI Engineer at 1POINT1 focused on shipping production systems with end-to-end ownership.
I handled solution design, implementation, and delivery while collaborating with cross-functional teams to align engineering execution with business goals.
- Scope: Building production AI systems including NL-to-SQL platforms, document intelligence workflows, and enterprise automation pipelines.
- Ownership: architecture, implementation, and rollout
- Context: high-impact business workflows and reliability requirements
- Business pain point addressed by: Built NL→SQL platform with schema-aware guardrails enabling non-technical teams to query PostgreSQL, MySQL, and MSSQL via natural language.
- Business pain point addressed by: Designed hybrid AI document intelligence system extracting 80+ structured fields from enterprise bidding documents using rule-based parsing + selective RAG.
- Business pain point addressed by: Automated large-scale document workflows via parallel PDF splitting + Google Drive pipeline, processing 1,000+ page files in under 2 minutes.
- Business pain point addressed by: Delivered computer vision POC for automobile defect classification achieving 85%+ accuracy across 20 job categories.
Project 1
- Business problem: Manual or slow workflow was limiting business throughput.
- My contribution: Built NL→SQL platform with schema-aware guardrails enabling non-technical teams to query PostgreSQL, MySQL, and MSSQL via natural language.
- Technologies: Python · FastAPI · PostgreSQL · Azure OpenAI · RAG
- Outcome: Delivered measurable reliability and speed improvements.
Tender Intelligence System
- Business problem: Manual or slow workflow was limiting business throughput.
- My contribution: Designed hybrid AI document intelligence system extracting 80+ structured fields from enterprise bidding documents using rule-based parsing + selective RAG.
- Technologies: Python · FastAPI · PostgreSQL · Azure OpenAI · RAG
- Outcome: Delivered measurable reliability and speed improvements.
Automation Pipeline
- Business problem: Manual or slow workflow was limiting business throughput.
- My contribution: Automated large-scale document workflows via parallel PDF splitting + Google Drive pipeline, processing 1,000+ page files in under 2 minutes.
- Technologies: Python · FastAPI · PostgreSQL · Azure OpenAI · RAG
- Outcome: Delivered measurable reliability and speed improvements.
Automation Pipeline
- Business problem: Manual or slow workflow was limiting business throughput.
- My contribution: Designed high-performance concurrent processing pipelines for enterprise automation across large unstructured datasets.
- Technologies: Python · FastAPI · PostgreSQL · Azure OpenAI · RAG
- Outcome: Delivered measurable reliability and speed improvements.
- Automated large-scale document workflows via parallel PDF splitting + Google Drive pipeline, processing 1,000+ page files in under 2 minutes.
- Designed high-performance concurrent processing pipelines for enterprise automation across large unstructured datasets.
- Built NL→SQL platform with schema-aware guardrails enabling non-technical teams to query PostgreSQL, MySQL, and MSSQL via natural language.
- Designed hybrid AI document intelligence system extracting 80+ structured fields from enterprise bidding documents using rule-based parsing + selective RAG.
- Designed high-performance concurrent processing pipelines for enterprise automation across large unstructured datasets.
Challenge
- Built NL→SQL platform with schema-aware guardrails enabling non-technical teams to query PostgreSQL, MySQL, and MSSQL via natural language.
Constraints
- Production reliability and latency expectations
- Cross-team coordination and evolving requirements
Solution
- Designed pragmatic architecture tradeoffs to balance quality, speed, and maintainability.
Outcome
- Shipped stable systems with measurable operational improvements.
- Used retrieval-augmented workflows when explainability and updatable knowledge mattered more than model re-training.
- Preferred relational storage for correctness, querying flexibility, and operational familiarity.
- Adopted asynchronous/concurrent processing where throughput and responsiveness were critical.
- Used hybrid extraction approaches to improve reliability across semi-structured inputs.
- Business impact comes from system reliability and usability, not model novelty alone.
- Clear ownership boundaries speed up delivery in multi-system initiatives.
- Incremental architecture decisions compound into maintainable platforms.
- Designed hybrid AI document intelligence system extracting 80+ structured fields from enterprise bidding documents using rule-based parsing + selective RAG.
- Automated large-scale document workflows via parallel PDF splitting + Google Drive pipeline, processing 1,000+ page files in under 2 minutes.
- Delivered computer vision POC for automobile defect classification achieving 85%+ accuracy across 20 job categories.
AI
- Azure OpenAI
- RAG
- LangChain
- Computer Vision
- Hybrid Extraction
Backend
- Python
- FastAPI
- REST API
- Google Drive API
Database
- MySQL
- Microsoft SQL Server
Infrastructure
- Git
- Docker
- Concurrent Processing
Tools
- PDF Processing
- Rule-based Parsing
- PostgreSQL