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Vikash Mediboina
Mid-level Machine Learning & Full-Stack Engineer specializing in GenAI platforms
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LLM/agent builder who has shipped production AI systems in the wellness space, including an LLM-powered food tracking product used by 5000+ users and a voice/call-routing onboarding workflow using LangGraph/LangChain with LiveKit and Twilio. Strong focus on practical reliability work: latency reduction, retrieval/embedding tuning, and CI-driven evaluation with simulations and metrics.
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Key Strengths
- Built and deployed LLM-powered food tracking system serving 5000+ users
- Reduced LLM initialization latency by switching to Agno and using pooled connections
- Improved retrieval accuracy by refining embeddings and increasing context length for better item selection
- Designed graph-based agent workflows by mapping user flows into node/task structures
- Integrated orchestration frameworks with real-time voice/calling stack (LangGraph/LangChain + LiveKit + Twilio) for onboarding and call routing
- Uses automated CI + simulations and precision/recall metrics to test and evaluate agent reliability
- Shipped multiple production microservices (7) with 50–60+ API endpoints
- Scaled backend via containerization and autoscaling based on request volume
- Performance optimization using caching and increased worker concurrency (Gunicorn)
- Designed relational schemas with ERDs and managed migrations with Alembic
- Improved slow analytics/insights queries using materialized views with scheduled refresh
- Built and shipped LLM-powered food tracking and meal-planning workflows with measurable time savings (3–4 steps to 1; coach time reduced to 5–10 minutes)
- Implemented LLM guardrails and observability/audit logging (Langfuse, input/output + token tracking)
- End-to-end ownership (frontend, backend, deployment/ops) as a founder
- Shipped and operated a production product with 7,000+ users and 500+ paying customers
- Designed and delivered a continuous-play workout video feature with frontend caching to reduce API calls
- Built and operated microservices on AWS Fargate with Terraform and CloudWatch
- Early-stage startup execution from idea to full product across 4 zero-to-one startups
- Postgres schema/migrations managed with Prisma
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