SD
Santoshi D
As a Gen AI Engineer, I am driven by a highly logical and meticulous approach, ensuring accountability and precision in every project. My concentrated focus and pragmatic mindset allow me to master complex challenges, consistently delivering robust and effective solutions.
Contact details — on requestProof of Work — on request
Experience
C
Gen AI Engineer
COGNIZANT · Jul 2023 – Present
- Built a GenAl-powered document intelligence pipeline to process personal loan applications, automating extraction and validation of bank statements, pay stubs, government-issued ID, Social Security verification, and employment proof.
- Designed an end-to-end pipeline covering document ingestion, OCR/Document Al extraction, LLM-based structuring, data validation, and handoff to loan officers.
- Enabled the GenAl layer to identify salary, employer, monthly obligations, existing loan payments, bank balances, bounced payments, inconsistencies, and missing documents.
- Generated concise, structured loan-application summaries for the credit team, reducing manual document-review effort and turnaround time.
- Built a Retrieval-Augmented Generation (RAG) copilot on Azure OpenAl (GPT models) and Claude models to assist credit analysts and underwriters processing 40-50 applications daily, supporting rather than replacing the final credit decision.
- Unified applicant data, credit bureau data, bank statements, existing loans, internal credit policy, and product rules into a single retrieval context, using a document chunking strategy tuned for policy and financial documents to improve retrieval precision.
- Used RAG to retrieve relevant underwriting policies and an LLM to generate structured risk assessments, including risk level, debt-to-income ratio checks, credit score policy checks, delinquency flags, and recommended next actions.
- Evaluated copilot output quality using RAGAS metrics (faithfulness, answer relevancy, context precision) alongside an LLM-as-a-Judge framework, continuously validating retrieval and generation quality before production release.
- Delivered explainable, policy-grounded risk summaries that accelerated underwriter decision-making while keeping a human in the loop.
- Designed a compliance-constrained, multi-agent early-stage collections system for accounts with missed loan payments across a portfolio of thousands of active loans and credit cards.
- Evaluated payment history, days past due, outstanding amount, customer communication history, prior promises-to-pay, and risk segment to determine the next permitted action.
- Automated a compliant, day-by-day collections cadence, including payment reminders, payment status checks, approved follow-up communications, and eligible payment-assistance offers, with automatic escalation of higher-risk accounts to a human collections officer.
- Built a multi-agent architecture consisting of a Collections Orchestrator, Delinquency Analysis Agent, Customer Segmentation Agent, Communication Agent, Payment/Promise-to-Pay Agent, and Escalation Agent, using LangGraph and CrewAl to define agent state, task-delegation, and escalation logic within compliance guardrails.
- Designed system-level and task-level prompts aligned with banking compliance standards, embedding guardrails to prevent hallucination, scope drift, and non-compliant or out-of-policy responses.
- Implemented structured JSON-output enforcement and input/output validation for underwriting and risk workflows to ensure downstream systems received consistent, machine-readable results.
- Implemented LangFuse and LangSmith for end-to-end LLM observability, tracking prompt performance, response quality, latency, and token usage to surface hallucinations and optimize reliability in production.
- Established an LLM-as-a-Judge evaluation workflow paired with the RAGAS library to continuously score prompt and RAG changes before rollout, reducing regressions in production.
- Conducted iterative prompt experimentation and response scoring to optimize accuracy, reduce operational risk, and continuously tune guardrail effectiveness.
- Adopted Al-assisted coding tools, including GitHub Copilot and Claude Code, to accelerate feature development, test-case generation, and code review across GenAl services.
H
UI Developer
Hertz · Jul 2023 – Dec 2023
- Developed responsive, reusable Ul components in React and JavaScript for Hertz's customer-facing car rental reservation and booking platform.
- Built and maintained key booking-flow screens, including vehicle search, rate and add-on selection, and checkout, ensuring a consistent experience across desktop and mobile.
- Integrated frontend components with backend REST APIs, referencing Swagger/OpenAPI specs and using Postman for request validation and debugging, to display real-time vehicle availability, pricing, and reservation status.
- Collaborated with UX designers and backend engineers to translate wireframes into pixel-accurate, accessible Ul implementations.
- Optimized page load performance and conducted cross-browser and cross-device testing to ensure consistent rendering across platforms.
- Participated in code reviews and contributed to a shared component library to improve Ul consistency and development velocity across teams.
Skills 0 proven through work
Also works with
UI Implementation from DesignCross-Departmental CoordinationInput Validation ImplementationAPI Validation TestingAPI Testing with PostmanAPI Documentation with Swagger/OpenAPIAPI IntegrationCross-Platform DevelopmentResponsive UI ImplementationApplication MaintenanceFront-end DevelopmentCSSHTMLHuman-in-the-Loop Review DesignHandling Data SparsityPrompt EngineeringVector Database UsageText EmbeddingDocument ChunkingData CollectionData Discrepancy InvestigationData ValidationBusiness Rules VerificationData Point IdentificationLLM Application IntegrationIntelligent Document ProcessingOCR System DevelopmentAI Solution ImplementationGenerative AI ApplicationAI Model TestingLangsmith UsageLLM-as-a-Judge EvaluationRagas Framework UsageClaude UsageOpenAI UsageFastAPI DevelopmentPython DevelopmentMulti-Agent System IntegrationPolicy ImplementationData-Driven RecommendationsRisk and Controls AssessmentAutomated Summarization SystemsDocument VerificationLLM-based Data Extraction
Proof of Work
Proof of Work
Santoshi shares this with people who ask. You'll hear back either way.
Education
B.Tech, Electronics & Communication Engineering
JNTUK University · 2019 — 2023
Contact details
Contact details
Santoshi shares this with people who ask. You'll hear back either way.
What drives their work
The Architect
You design and construct robust systems that stand the test of complex data and user demands.
Santoshi's superpower
You excel at engineering reliable, end-to-end technical solutions that meticulously handle complex data and user interactions.
How Santoshi works
You would excel in teams focused on building robust, high-integrity software solutions, particularly where data accuracy and system reliability are paramount.
Ask about this candidate
AI responses may contain errors. Proof status and source information are provided by Proof of Skill.
A Proof CV — one profile, kept current, with the proof attached. Back to the portal