AI Engineer with 4+ years in Agentic AI, Generative AI & Machine Learning.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI Developer with 4+ years of experience building production-grade AI, Agentic AI, and Generative AI systems using LLMs, RAG architectures, and data-first, rule-driven AI solutions across Machine Learning, Deep Learning, and analytics. Hands-on with agent-oriented workflows, self-hosted LLaMA, prompt engineering, and semantic AI pipelines for explainable and controllable GenAI systems. Experienced in developing backend AI services, agentic risk and compliance engines, and real-time AI systems on cloud and Linux servers. Collaborates effectively with product, backend, and business teams to deliver scalable, compliant, enterprise-ready AI platforms.
RGPV University
Master of Technology (M.Tech) · Digital Communication
January 1, 2019 – January 1, 2021
CSVTU University
Bachelor of Engineering (B.E.) · Electronics & Telecommunication
January 1, 2009 – January 1, 2013
Synkcode FZCO
AI Developer | Generative AI & Backend Systems
September 1, 2025 – Present
India
Codenatics Technologies Private Limited
Data Scientist & Analyst
August 1, 2021 – September 1, 2025
India
AI-Driven Excel Analytics System Using LLM, RAG & SQL (Generative AI)
April 30, 2026 – Present
Objective: Build a data-first analytics system where SQL and deterministic code act as the source of truth, and LLMs are used for structure understanding and explainable insights. Key Contributions: Implemented Excel ingestion and schema detection using LLaMA Sheets with rule-based fallback for safe data loading. Built SQL-based data normalization, validation, and analytics pipelines, ensuring LLM-free numerical computation. Designed a RAG pipeline storing verified facts in a vector database and used LLMs only for fact-based explanations. Impact: Delivered a reliable, production-ready GenAI analytics platform with hallucination-free insights, auditable results, and JSON-based outputs for seamless integration.
AI Trade Advisor – LLM-Based Customs Declaration Agent (Generative AI)
April 30, 2026 – Present
Objective: Build an AI-powered declaration agent that creates complete customs declarations with minimal user input, reducing filing time from 5-10 minutes to 1-2 minutes for repeat traders. Key Contributions: Developed an LLM-driven conversational agent that determines required fields, asks only mandatory questions, and autofills remaining data from system history. Implemented a rule engine to enforce declaration-specific business rules, validations, and conditional logic before API submission. Built an API orchestration layer to call declaration APIs in sequence, manage ID dependencies, and handle validation errors with adaptive re-prompts. Impact: Delivered a production-ready GenAI trade solution with 70-90% autofill, 4–6 user inputs per declaration, reduced API failures, and significantly improved filing speed and user experience.
Agentic Risk Intelligence System (Agentic AI)
April 30, 2026 – Present
Objective: Build a deterministic, explainable risk assessment system where rule-based logic is the source of truth and agentic AI is used for parallel risk analysis and compliance reporting. Key Contributions: Designed planner-driven workflows for dynamic risk checks and cost optimization Implemented parallel swarm agents for anomaly, pattern, and similarity detection Built specialist verification agents for sanctions screening using governed tool access Developed deterministic risk aggregation producing auditable decisions (Approve/Flag/Block) Generated compliance-ready explanations using template-based and LLM-assisted approaches Impact: Delivered a scalable, regulator-ready AI risk platform with full auditability, controlled AI usage, and explainable decision-making.
Country of Origin-Based Trade Risk Scoring Engine (Analytics & Compliance AI)
April 30, 2026 – Present
Objective: Build a trade risk scoring system to calculate Country of Origin risk using HS code normalization, historical trade patterns, and declared value deviation analysis. Key Contributions: Developed HS code format detection and normalization logic to standardize product classification and enable accurate country-level historical comparisons. Implemented a multi-factor risk scoring model combining country frequency, HS code rarity, route risk, transport mode risk, intelligence flags, and value abnormality. Designed a weighted scoring framework to convert analytical signals into a single explainable Country of Origin risk score Impact: Delivered a production-ready, explainable risk scoring engine enabling early fraud detection risk-based inspection prioritization in customs workflows.
Cultural Fit Analysis
The psychometric test score of 0 raises serious concerns about the candidate's cultural fit, specifically regarding their ability to collaborate, handle stress, and demonstrate a positive work attitude. This score suggests fundamental challenges in aligning with typical professional workplace expectations. A re-evaluation of these attributes is essential.
Soft Skills & Operational Fit
The candidate's psychometric test score of 0 indicates significant concerns regarding logical reasoning, work attitude, stress handling, and team collaboration. This score suggests a potential severe mismatch with operational fit and team dynamics. Further assessment or re-testing in these areas is critically needed.