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AI Engineer with 2+ years in ML & LLM Pipelines and MLOps
Junior AI Engineer with 1+ year of hands-on experience building end-to-end ML and LLM-powered pipelines for real business workflows. Proficient in Python, Scikit-learn, TensorFlow, and Hugging Face Transformers. Experienced in the full ML lifecycle – data collection, preprocessing, feature engineering, model training, evaluation, and API deployment. Familiar with local LLMs via Ollama, MLflow for experiment tracking, Docker, and REST API integration. Strong communicator comfortable working in teams and presenting AI solutions to non-technical stakeholders. Based in Kerala available for WFO in Trivandrum. Immediate joiner.
Jain University, Bengaluru
B.Tech · Robotics and Automation
August 1, 2019 – June 30, 2023
Coderzon
AI/ML Engineer
September 1, 2024 – Present
India
Pacific Weld Systems Pvt. Ltd.
Robotics & Automation Engineer
August 1, 2023 – August 1, 2024
Chennai, Tamil Nadu, India
Enterprise RAG System (Banking)
September 1, 2024 – Present
Led full pipeline development: document ingestion → text preprocessing → vectorization (Sentence-Transformers all-MiniLM-L6-v2) → FAISS retrieval → LLM response generation (LLAMA 3.3 70B). Set up MLflow experiment tracking, model versioning, and evaluation metrics from scratch; containerized with Docker for production deployment via FastAPI. Documented data flows, retrieval assumptions, model limitations, and bias considerations; presented solution demos to stakeholders and iterated based on business feedback.
AI-Powered Job Matching Platform
September 1, 2024 – Present
Built end-to-end ML pipeline: raw data ingestion → NLP preprocessing → feature engineering → embedding-based similarity scoring (Hugging Face) → supervised ranking model → REST API output. Applied unsupervised clustering on candidate embeddings to group similar profiles; evaluated with precision/recall and consistency metrics; hybrid semantic + rule-based ranking outperformed keyword baseline. Integrated into a web backend via FastAPI; documented model assumptions, evaluation results, and limitations for review.
Agentic Bus Booking System
September 1, 2024 – Present
Built a fully automated conversational workflow system using LLAMA 3.3 70B with custom function-calling tools — autonomous multi-step task execution (availability check, seat allocation, booking confirmation, delay handling). Implemented NLP-based intent detection and entity extraction from natural language input; designed for practical business workflow automation with MySQL backend and REST API deployment. Added multilingual support (English & Malayalam); documented system behavior, edge cases, and known limitations.
AI-Powered Order Management System (Eyewear)
September 1, 2024 – Present
Designed and built a production-deployed order management system for an eyewear brand covering the full order lifecycle: intake, inventory tracking, SLA monitoring, risk prediction, and alert generation. Implemented a Rule-Based AI Prediction Engine that classifies orders as HIGH, MEDIUM, or LOW risk based on inventory availability, remaining SLA time, and order processing stage — providing explainable, real-time predictions without requiring historical training data. Built automated alert generation triggering on HIGH-risk orders and SLA breaches; designed for integration with email, WhatsApp, and Slack notification systems. Deployed on Render with full REST API via FastAPI and Swagger UI documentation; architected with a modular design to support future ML model upgrades (Random Forest, XGBoost, LLM-based assistants) without API changes.
Cultural Fit Analysis
The candidate's diverse project portfolio, ranging from job matching and bus booking to order management and enterprise RAG systems, indicates adaptability and a broad interest in applying AI across different domains. Their proactive approach to MLOps and continuous learning aligns well with an innovative and growth-oriented culture. The experience in presenting solutions to non-technical stakeholders also suggests a collaborative and business-focused mindset.
Soft Skills & Operational Fit
The candidate demonstrates strong initiative by setting up MLOps practices proactively and being a self-driven learner. Their experience in collaborating with cross-functional teams and communicating complex AI concepts to non-technical stakeholders indicates good team collaboration and operational fit. The previous role in Robotics & Automation also suggests a systematic approach to problem-solving and debugging, which is valuable in ML pipeline troubleshooting.