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Bolanwar Gokul
As an AI Engineer, I am driven by a deep sense of accountability and ambition, always striving for mastery in my field. I approach complex challenges with a highly logical and insightful mindset, committed to developing pragmatic and effective solutions.
Identity verified by DigiLockerDegree confirmed by Malla Reddy College of Engineering and Technology, Sri Gayathri Junior College
Contact details — on requestProof of Work — on request
Experience
V
AI Engineer
VEAI Private Limited · Aug 2024 – Aug 2026
- Multi-Agent GenAI Platform: Architected a multi-layer multi-agent GenAI platform using LangChain and LangGraph, with agent planning, task decomposition, tool execution, and workflow orchestration powered by AWS Lambda and SQS; supported 15+ parallel workflows with approximately 5-second execution and 95% task accuracy.
- AI Memory System: Built a centralized AI Memory System processing user activity across emails, calendars, tasks, integrations, and documents using Vector DB, Neo4j Knowledge Graphs, and Google's Open Knowledge Format (OKF) to provide personalized, persistent, and user-isolated context across AI agents and workflows.
- Multi-Provider LLM Gateway: Architected a centralized LLM Gateway supporting multiple providers and API keys with intelligent routing, key rotation, and fallback mechanisms to improve availability and prevent provider rate-limit failures; achieved approximately 40% lower latency and 30% lower LLM cost.
- Enterprise RAG & Search: Engineered an enterprise RAG and search platform supporting 50K+ documents using embeddings, ANN retrieval, hybrid search, RRF, and Vespa, achieving <500 ms retrieval latency and approximately 40% improved retrieval relevance through retrieval and ranking optimization.
- Document Intelligence: Built a document intelligence pipeline processing 100K+ unstructured documents and extracting structured information with 90%+ accuracy; evaluated Pinecone, Weaviate, MongoDB, and Vespa to support scalable, multi-tenant document retrieval and downstream AI applications.
- Multi-Tenant AI Workspace: Developed a multi-tenant AI workspace combining Calendar, Task, Search, Lead, Proposal, and Contract agents with secure integrations across Gmail, Outlook, Google Calendar, GitHub, HubSpot, Zoho, and MongoDB, enabling contextual actions across communication, productivity, CRM, and business workflows.
- MCP Tool Orchestration & Dynamic AI Skills: Designed an internal Model Context Protocol (MCP)-based tool orchestration framework enabling agents to securely discover and invoke integration tools, while developing dynamic user-specific AI skills that generate isolated capabilities based on individual requirements and workflow context.
- Build Your Own Agents Platform: Built a Build Your Own Agents platform enabling users to create configurable AI agents, connect enterprise integrations, define available tools and context, and automate business processes through schedule-, event-, and condition-based workflows without requiring manually orchestrated multi-step execution.
- Meeting Intelligence: Delivered real-time Meeting Intelligence for macOS and Windows by combining live transcripts with on-demand screen context to provide contextual research and suggested responses during meetings, while generating post-meeting summaries and action items with approximately 3–5 second response times.
- Email Intelligence: Developed an Email Intelligence system supporting 11+ configurable email categories, personalized AI-generated replies, and contextual email understanding; integrated email, calendar, task, and lead information to enable intelligent actions and automated workflows beyond traditional email classification.
- Proactive Intelligence: Developed Proactive AI capabilities that continuously leverage context from emails, calendars, tasks, and leads to identify relevant user needs, generate daily planning recommendations, and surface contextual next actions, enabling personalized assistance without requiring users to explicitly initiate every workflow.
- Computer Use, One-Tap Prediction & AI Dictation: Built Computer Use capabilities for natural-language computer automation alongside One-Tap Prediction and AI Dictation for context-aware text generation, correction, and insertion, enabling low-friction interaction between AI capabilities and everyday desktop applications.
- AI Browser Agent: Developed and benchmarked a privacy-focused AI Browser Agent with local execution, browser-context understanding, intelligent action planning, and BYOK support, enabling natural-language browser automation while achieving 98% scores across key browser-agent evaluation scenarios through systematic benchmarking and optimization.
Skills 0 proven through work
Also works with
Taking OwnershipUptime ManagementRapid Technology OnboardingAWS DeploymentDockerVector Database UsageFastAPI DevelopmentAI Model OptimizationAI Model TestingEnd-to-End Data Science Lifecycle ManagementCross-Departmental CoordinationTool DevelopmentSaaS Platform DevelopmentTest Case EvaluationML Detection Speed OptimizationTrade-off AnalysisQuery Relevance OptimizationTechnical TroubleshootingAdaptabilityCritical ThinkingHypothesis-Driven ExperimentationRequirements AnalysisWork Planning and PrioritizationConsistent Quality DeliveryTechnical ContributionProduct DevelopmentSemantic SearchDocument Intelligence Platform DevelopmentTechnical Solution DesignProblem SolvingAI Agent DevelopmentMLOps ImplementationRetrieval-Augmented GenerationGenerative AI ApplicationApplication MaintenanceScalable System DesignModel AdaptationDecision MakingRetrieval Quality EvaluationObservability Platform DevelopmentEvaluation Framework DesignFailure Mode AnalysisModel Metric Selection & InterpretationPerformance Metrics AnalysisGemini AI IntegrationClaude UsageOpenAI UsageTiered Model RoutingMulti-Core ProcessingProcess ImprovementFault-Tolerant System DesignAI Solution ImplementationLLM Cost OptimisationModel Performance ImprovementLow-Latency System OptimizationModel Selection Trade-Off AnalysisApplication Performance OptimizationFallback Mechanism DesignLLM Application IntegrationError HandlingApache Airflow OrchestrationPrompt EngineeringAI Platform Architecture DesignAmazon SQS IntegrationAWS LambdaEvent-Driven ArchitectureLangGraph Framework UsageLangChain Framework UsageAI Workflow AutomationMulti-Agent System IntegrationAI Platform Development
Proof of Work
Proof of Work
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Education
B.Tech, Computer Science (Data Science)
Malla Reddy College of Engineering and Technology · 2020 — 2024
Intermediate Education, MPC
Sri Gayathri Junior College · 2018 — 2020
Projects
Contact details
Contact details
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What drives their work
The Architect
They design and construct AI systems that are built to last and perform under real-world conditions.
Bolanwar's superpower
You excel at engineering resilient and high-performing AI systems from concept to production.
How Bolanwar works
You are best suited for roles in engineering teams focused on building and scaling complex AI products.
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