I work with enterprise customers to get AI systems into production. Most projects start with a loosely defined business problem and a lot of existing infrastructure. My job is to figure out what should actually be built, build it, connect it to the customer's data and systems, and stay involved until their team is using it day to day.
I came into this from data engineering, so I spend more time than most on the data and integration side of AI projects. In my experience that's where things usually break.
Customer problem → Discovery → Architecture → Prototype → Data → AI → Integration → Production → Adoption
- Technical discovery with customers: understanding the problem, the data they have, their constraints, and what "working" means to them
- Architecture and solution design for AI systems that need to fit into an existing enterprise environment
- Prototypes and proofs of concept, and turning the ones that work into production deployments
- RAG systems, LLM applications, and agent workflows, mostly on Claude and OpenAI models
- Prompt engineering and evaluation. I build eval sets from real usage so prompt and model changes don't quietly break things
- Data pipelines, migrations, and validation to get customer data into a usable state
- Integrations with the APIs and systems customers already run
- Troubleshooting across the stack when something in production isn't behaving
| AI / GenAI | LLMs, RAG, AI agents and agentic workflows, prompt engineering, LLM evaluation, MCP, Guardrails, Claude, OpenAI, Cursor |
| Data | Python, SQL, ETL / ELT, data pipelines, data migration and transformation, data validation and quality, large-scale data processing, relational databases |
| Cloud | AWS, Azure, cloud architecture, data lakes, APIs, system integration, enterprise architecture, on-prem to cloud migration |
| Solutions work | Solution architecture, technical discovery, requirements gathering, proofs of concept, enterprise deployments, troubleshooting, customer adoption, working across engineering, product, and customer teams |
Before moving into AI solutions work I was a data engineer at Walmart Global Tech (ETL on GCP, PySpark pipelines, data migration) and at Capgemini (Spark, data lakes, Azure Data Factory automation). At BrainChip I was an ML Solutions Architect working on LLM fine-tuning, RAG pipelines, and voice solutions for customers. MS in Computer Science and Engineering from the University at Buffalo.
I'm in the process of putting some of my own work up here. Nothing worth pointing at yet.
- LinkedIn: linkedin.com/in/dhvanik
- Email: dhvanikothari03@gmail.com