Subrata Mondal
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Experience
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FOUNDING AI ENGINEER
LawWorld · Aug 2024 – Jun 2026
- Architected an Al Native Legal Tech Product end-to-end - production Al applications and scalable backend systems.
- Designed Agentic Al workflows and RAG pipelines serving 2,000+ users over a 170K-document corpus, prioritizing reliability, latency, and operational efficiency.
- Replaced a $12K/year vendor subscription in-house solo-building the agentic rich-text editor backend and its drafting orchestration layer - TipTap's paid Al toolkit only covered the editor surface, not the differentiating logic.
- Saved ~$500/month in cloud overhead building durable execution directly on MongoDB checkpoints and Azure Service Bus instead of adopting Temporal sustains 3-hour long-running agents with automatic crash-resume and zero data loss.
- Standardized on Model Context Protocol (MCP) for secure tool access deployed Dockerized servers in client VPCs with SSE tunnels for JSON-RPC schema discovery, executing agents without hardcoded tools or raw database credentials.
- Gated architecture decisions on strict blind-evals – defined accuracy/cost thresholds upfront against 550 production documents, forcing a data-driven choice between ReAct and structured-output rather than rationalizing decisions post-hoc.
- Held P99 latency at 50ms via an L2 Redis Stack semantic cache.
- Achieved a 20-30% hit rate on repetitive queries, offsetting ~$15,000/month in LLM inference spend using a $250/month cache at million-call scale.
- Fan-out parallel search cut retrieval latency 1.3s to 443ms then closed the multi-tenant gap structurally - session junctions are injected server-side into the vector pre-filter, so a query can narrow the search space but never widen it across tenants.
- Cut per-unit inference cost 5-10x via Swarm model-tiering - swapped GPT-5.2 for GPT-5.4-mini in extraction while keeping Qwen 3.5 for routing.
- Validated against versioned pricing across hundreds of legal Acts, scaling toward India's full statutory corpus.
- Blocked hallucinated-package execution across federated agents - implemented deterministic glass-box gates on an Agent-to-Agent (A2A) bus, running dependency-provenance checks against an allow-list before any untrusted payload executes.
Founding AI Engineer
lawworldai · Aug 2024 – Jun 2026
- Architected and deployed deterministic agentic AI systems, focusing entirely on infrastructure reliability, evaluation metrics, and unit economics.
- 🚀 Agentic Substrate & Orchestration:
- ▸ Built Primitive-Level Agentic Orchestration: Architected the 8 core agentic primitives from scratch in async Python—including a three-axis loop (tokens, turns, time).
- Achieved a 7–8× wall-clock speedup (700s → 100s) on Pass 1 extraction via asyncio.gather + Semaphore(6).
- ▸ Enforced Strict Data Contracts: Designed a multi-agent swarm utilizing production tool-calling with strict Pydantic V2 JSON validation to handle high-entropy reasoning tasks.
- ▸ Architected Durable Execution: Implemented MongoDB per-turn checkpointing combined with Azure Service Bus choreography to guarantee at-least-once execution and zero data loss on node crashes.
- 📊 Ops-to-Research Evaluation & FinOps:
- ▸ Engineered Evaluation Infrastructure: Designed an offline DSPy Extractor-Judge evaluation graph that enforced a 0.90 Cohen's Kappa agreement baseline against human golden datasets.
- ▸ Deployed Autonomous GEPA Loops: Implemented self-healing pipelines that autonomously rewrite agent instructions based on trace failures.
- ▸ FinOps Cognitive Routing: Shipped a multi-provider LiteLLM proxy router that dynamically shifts workloads based on cognitive demand.
- Processed 15,775 pages pushing 100.8M tokens with a 47.6% prompt-cache hit rate, achieving a total inference cost of just $296.75.
- 🛠 Substrate Stack: Python 3.12+ (Async) · FastAPI · Docker · Kubernetes + KEDA · Terraform · Redis · MongoDB Atlas · DBOS Durable Execution · LiteLLM · DSPy · Pydantic V2 · OpenTelemetry (OTel) · structlog · Model Context Protocol (MCP) · Azure/AWS
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Python Developer
The AlgoHype · Jul 2024 – Sep 2024
- 3-month engagement (pre-lawworld.ai) — built two production AI services:
- ▸ Synthetic data pipeline for LLM fine-tuning — Azure OpenAI for synthetic Q&A pair generation against a domain corpus.
- Document ingestion via python-docx + pymongo, Pydantic schema validation + content-relevance + duplicate-detection quality gates, LangFuse for prompt tracing, backoff library for rate-limit retry, Docker for deployment.
- ▸ Context-aware QA microservice on FastAPI + Docker — multi-provider LLM routing (OpenAI + Groq) via LangChain, Qdrant vector DB for similarity search, Pydantic-typed API contracts, LangFuse instrumentation, Azure Pipelines for CI/CD.
- 🛠 Stack: Python, FastAPI, Docker, MongoDB, Pydantic, Qdrant, LangChain, LangFuse, OpenAI, Groq, Azure OpenAI, Azure Pipelines.
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Python Developer
AlgoHype Analytics · Jul 2024 – Aug 2024
- Engineered synthetic-data generation pipelines using Azure OpenAI and shipped a QA microservice for automated data validation.
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Junior Machine Learning Engineer
Omdena · Nov 2023 – Jan 2024
- 8-week AI Innovation Challenge with Omdena (community-led, volunteer-basis): collaborated on an LLM-powered interview-prep chatbot — speech input, structured assessment, feedback loop.
- NLP + RAG + prompt engineering.
- Demo: https://drive.google.com/file/d/1EDcnxE53pu77E4FJsVt4BMqZIWb5WZMu/view
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ML Engineer
Omdena · Nov 2023 – Dec 2023
- Developed an LLM-powered interview-prep chatbot, architecting the conversational flow and retrieval logic.
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Data Analyst
Trainity · Apr 2023 – Nov 2023
- 8-month part-time structured training in data analysis + machine learning (concurrent with final-year BTech).
- Capstone projects: customer-churn prediction model, marketing-campaign optimization analysis, interactive dashboard suite.
- Stack: Python, SQL, Excel, Tableau.
Skills 0 proven through work
Also works with
Durable Execution ImplementationAmbiguity ManagementDeterministic System DesignCloud ComputingObservability Platform DevelopmentSoftware Quality AssuranceSystem EnhancementTool DevelopmentTechnology Stack SelectionSoftware Development PracticesError HandlingTechnical TroubleshootingApplication State ManagementTechnical Solution DesignEvaluation Framework DesignStrategic PlanningSecurity ArchitectureAI Platform OptimizationPrivacy by DesignDevOps ImplementationResponsible AI UseApplication DeploymentProblem SolvingInfrastructure ManagementGreenfield DevelopmentCoherence Kappa Calculation & InterpretationAzure Key Vault UsageKnowledge Base Design & ManagementAdaptive Control System DesignConversational UX DesignChatbot DevelopmentMachine Learning ApplicationFunctional TestingExploratory Data Analysis (EDA)Automation Test DevelopmentLimited Dataset HandlingAPI Response Contract DesignData File CreationData Integrity ManagementData ValidationAzure AI Services IntegrationSynthetic Data GenerationCosine SimilaritySecure Data HandlingNatural Language GenerationWorkflow State ManagementData Partitioning and Segregation DesignSecurity Policy ApplicationDependency ManagementReproducible Deployment ImplementationLLM-based Data ExtractionLLM Application IntegrationQwen Model UsageLarge Language Model ConceptsMulti-Tenant Architecture DesignQuery Relevance OptimizationSemantic Caching ImplementationLow-Latency System OptimizationRabbitMQ Message Queue ImplementationAzure Service Bus IntegrationMongoDBWorkflow Engine DevelopmentLLM Cost OptimisationLLM Output ParsingCost-Benefit AnalysisAI Model TestingPrecision Metric InterpretationRetrieval Quality EvaluationQuery ExpansionSemantic SearchIterative Reasoning for AI AgentsRecall Metric InterpretationRagas Framework UsagePrompt EngineeringLLM-as-a-Judge EvaluationTraining Data CreationML Training & Testing PipelinesApplication Performance OptimizationReliability ImprovementRetrieval-Augmented GenerationEnvironment-Specific Configuration ManagementAPI Key ManagementCodebase ExtensibilityClean Code PracticesModular System DesignApplication ConsistencyModel Accuracy MaintenanceAI Hallucination DetectionBackend DevelopmentCorporate Sustainability AnalysisCloud Cost Estimation & ComparisonFault-Tolerant System DesignRegression TestingContext StructuringProcess ImprovementScalable System DesignMulti-Agent System IntegrationRedis Cache ImplementationSoftware Architecture PrinciplesTaking OwnershipAI Solution ImplementationProduct DevelopmentAI Platform Architecture DesignServerless ArchitectureAWS ECS UsageCI/CD Pipeline ImplementationGitHub ActionsAzure DeploymentDockerCodingAI Agent DevelopmentLangChain Framework UsageLangGraph Framework UsageEnd-to-End System UnderstandingAI Platform DevelopmentVector Database UsagePostgreSQL Database UsageMicroservices ArchitectureFastAPI DevelopmentPython Development
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Education
Bachelor of Technology, Computer Science Engineering (CSE)
Parul University · 2019 — 2023
Projects
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