Pavan Modi

As a Machine Learning Engineer with 3 years of experience, I am driven by a determined pursuit of mastery, constantly seeking autonomous challenges where I can apply logical, pragmatic solutions. I thrive on accountability and am ambitious in delivering impactful results, approaching complex problems with a focused yet relaxed demeanor.

Work style

Remote

Contact details — on requestProof of Work — on request

Experience

Machine Learning Engineer

Fuzzy Labs · Feb 2024 – Mar 2026

Not yet confirmed

Co-led a RAG system over sensitive UK government legal documents.
Used Qdrant as vectorDB and moved retrieval from semantic-only to hybrid (BM25 + dense) with reranking, fixing exact statute/clause lookups cosine search missed; measured the gain on NDCG@10 and MRR against a 660-query eval set built with government lawyers.
Served it with vLLM behind an API-gateway auth layer that rejected unauthenticated queries before any LLM spend, added semantic caching to serve repeated queries without re-hitting the LLM, with Langfuse tracing, cutting query latency ~16%.
Fine-tuned a pre-trained LLM (Mistral) on domain-specific UK legal data using QLORA (4-bit) to adapt it efficiently for training and production serving; evaluated the fine-tuned model against the RAG baseline and recommended against shipping after inference cost and latency did not justify the marginal business gain.
For a real-estate internal-document chat product, designed and shipped a graph RAG system (PostgreSQL with pgvector) to replace underperforming hybrid RAG after diagnosing the data as inherently relational.
Owned the evaluation, built the eval set, implemented hard-negative mining, and wrote LLM-as-judge scoring on context and answer relevance, run through the team's eval-gated CI before release.
Managed conversation context with query rewriting, HyDE, and reranked chunk shortlisting, plus a sliding-window memory that I extended with LLM summarization once the window alone lost earlier context.
Explored inference-optimization techniques, quantized an LLM to GGUF (llama.cpp) for on-device mobile deployment in a POC, evaluating the cost and privacy trade-offs of moving generation to the edge.
For an e-bike rental platform, built an event-driven pipeline with Kafka to stream and process a steady flow of rental, return, and availability events in near real time, with services communicating over gRPC and REST, Dockerised and deployed on AWS behind a load balancer, with blue-green deployment and Prometheus/Grafana monitoring.

Machine Learning Engineer

Barclays · Nov 2020 – Nov 2022

Not yet confirmed

Owned the full ML pipeline: Docker, MLflow for tracking, CI/CD with automated retraining and rollback.
A/B tested new model versions against production baselines before rollout.
Cut deployment time from ~2 weeks to under 10 days.
Improved existing fraud detection models in a regulated banking environment using LightGBM and feature engineering over large transactional datasets (SQL), tuned for card fraud and suspicious transactions across 2.8M+ users, which reduced false positives by 12%.
Tried a deep learning approach but latency and cost did not justify the marginal gain so stuck with what worked.
Set up monitoring with Prometheus, Grafana and evidently ai for latency, drift, model performance dashboards.
Caught a distribution shift early that would have degraded fraud recall by ~8%.
Kept SLAs intact, saved compute costs by 15%.
Worked across risk, compliance, and product teams on loss-prevention workflows.
Every model decision needed documentation, audit trails, and explainability reports, not optional in banking.

Data Scientist

Digi-Soch · Oct 2019 – Oct 2020

Not yet confirmed

Built ETL pipelines in SQL and time-series forecasting models (ARIMA, Prophet) for retail and e-commerce clients.
The demand forecasting work fed directly into inventory planning, one client reduced overstock by ~18% (in Q1) after deployment.

Skills 0 proven through work

Also works with

MLflow UsageVLLM UsageDecision MakingMentoring and Peer CoachingData ExaminationConceptual UnderstandingPortfolio ManagementWealth ManagementProblem SolvingAdaptabilityBusiness AcumenStakeholder ManagementProduct LeadershipProcess ImprovementGPU ComputingOpen Source ContributionData ScienceMachine Learning ApplicationCompliance ManagementDeep LearningThreshold-Based AnalysisFeature EngineeringPrecision Metric InterpretationBackend DevelopmentFault-Tolerant System DesignGraph Databasespgvector UsagePostgreSQL Database UsageDatabase ManagementData ModelingCodebase Complexity ManagementLow-Latency System OptimizationLLM Cost OptimisationApplication DeploymentTechnical Solution DesignAI Model TestingAI Solution ImplementationETL Pipeline DevelopmentInsurance AnalysisProduct DevelopmentE-commerce Domain KnowledgeTime Series Trend AnalysisDockerCI/CD Pipeline ImplementationML Experiment TrackingLightGBM ModelingModel Performance ImprovementFintech Domain KnowledgeFraud DetectionStreaming Data ProcessingApache KafkaModel Accuracy MaintenanceAWS DeploymentMachine Learning Model DeploymentAI Platform Architecture DesignMLOps ImplementationApplication MonitoringApplication Performance OptimizationLLM Infrastructure DevelopmentData Search System DevelopmentChatbot DevelopmentTeam LeadershipNatural Language GenerationLarge Language Model ConceptsRetrieval-Augmented Generation

Proof of Work

Pavan shares this with people who ask. You'll hear back either way.

Education

MSc, Data Science

University of Greenwich, London · 2023 — 2024

Contact details

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