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AI Engineer with 1+ years in LLM & API Integration, Python & FastAPI
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Assessing your cultural and operational fit
Results-driven AIML Engineer with 1 year of production experience integrating LLMs and AI APIs into real-world backend systems. Proven track record shipping end-to-end intelligent applications from REST API design to multi-agent orchestration using Python, FastAPI, and OpenAI-compatible LLM APIs. Experienced in prompt engineering, Agentic AI workflows, scalable backend architecture, and rapid product delivery. Instinctively leverages AI tools to prototype and ship faster without sacrificing production quality.
RMK Engineering College
Bachelor of Technology · Artificial Intelligence and Data Science
August 1, 2020 – June 30, 2024
Avasoft
AIML Engineer
June 1, 2025 – Present
Chennai, Tamil Nadu, India
Lead Generation & Outreach Automation Platform
June 1, 2026 – Present
Built a multi-agent system using Strands Agents and PydanticAI for lead discovery, scoring, enrichment, and LLM-generated outreach emails reducing broker prospecting time by 70%. Shipped 6+ REST API endpoints for ICP management, lead search, enrichment, scoring, and campaign execution. Integrated LLM APIs for personalized outreach content generation, enabling brokers to send targeted emails to prospects at scale. Designed scalable data models and async workflows using Amazon SQS, processing 1,000+ messages/day. Achieved 99%+ workflow reliability through structured exception handling, input validation, and business rule enforcement. Maintained codebase with Docker, Git, and CI/CD pipelines for consistent deployment across environments.
Agentic AI Content Analysis Platform
June 1, 2026 – Present
Integrated LLM APIs to build a Sentiment Analysis Agent, Readability Agent, and Rewrite Agent automating content review across banking, healthcare, and retail sectors. Engineered multi-agent orchestration using PydanticAI, coordinating 3 specialized agents in a production pipeline, cutting manual review effort by 60%. Designed REST API endpoints using FastAPI for agent orchestration, integrated with client content pipelines. Applied prompt engineering to tune LLM outputs for production-readiness, improving consistency by 40% and reducing error rate to under 2%. Implemented validation mechanisms and guardrails to ensure reliable, safe AI-generated responses. Managed data persistence via SQLAlchemy ORM; used Git and CI/CD for version control and deployment.
Introduction to Machine Learning
NPTEL
June 1, 2026 – Present
Programming for Everybody (Getting Started with Python)
Coursera
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity across banking, healthcare, retail, and lead generation, combined with their proactive adoption of new technologies and collaborative approach, indicates a strong cultural fit for dynamic, innovation-driven environments. Their focus on delivering production-ready systems aligns well with a results-oriented culture.
Soft Skills & Operational Fit
The candidate demonstrates strong ownership, adaptability, and attention to detail, evidenced by delivering two end-to-end intelligent systems and rapidly onboarding new technologies. Their 'AI-First Mindset' suggests a proactive approach to leveraging AI tools for efficiency. Collaboration across engineering, product, and QA teams indicates good operational fit for agile environments.