viditparashar.me/servicesscoped per engagement
LLM feature build — a contained piece of AI work delivered into your codebase
Best for a team that knows what it wants built but has nobody free who has done it before.
What you get
- The feature, in your repository, reviewed by your team
- An evaluation harness, so the next change is measured rather than hoped for
- Explicit confidence and escalation behaviour instead of an implicit one
- A written account of what it does badly, which is the part that gets skipped
scoped per engagement
Related results
Results
- viditparashar.me/servicesviditparashar.me › services › review
AI feature review — a second opinion on an LLM feature before it reaches…
scoped per engagement
AI feature review — a second opinion on an LLM feature before it reaches users. Best for a team about to ship something they are slightly nervous about. A written…
- viditparashar.me/servicesviditparashar.me › services › full-time
Full-time roles — an AI or full-stack engineering role where LLM work is…
open to conversations
Full-time roles — an AI or full-stack engineering role where LLM work is the product, not a feature bolted on. Best for teams putting language models into something…
- Someone who has shipped LLM features into regulated environments, not just prototypes
- Comfort across the whole stack, from the speech pipeline to the operator UI
- A bias towards evaluation and explainability over demo quality
- Willingness to own the unglamorous half: handoff behaviour, thresholds, what happens at the edge
- viditparashar.meviditparashar.me › hire
Hire Vidit Parashar — open to interesting problems
ContactIST (UTC+5:30)
Currently open to interesting problems. Email viditsharma818@gmail.com — replies usually land within one working day, IST (UTC+5:30). Overlaps a full working day with…
Search this site for “LLM feature build — a contained piece of AI work”