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pradeep singam

@pradeepsingam

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Applied GenAI & AI engineer building RAG and agentic AI systems.

India

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What I'm looking for

I’m looking to build production-grade GenAI/AI systems—RAG, agents, and scalable low-latency APIs—solving real business problems. I value strong engineering practices, ownership, and measurable impact from prototype to deployment.

I’m an Applied GenAI & AI Engineer with 6+ years of experience building production-grade AI systems, including RAG pipelines, agent-assisted workflows, and multimodal knowledge assistants. I focus on NLP, LLMs, and computer vision, with a strong engineering mindset for scalable, low-latency AI services.

At Easy Hiring Infotech LLP, I converted complex Excel-based models into modular Python + DAG systems and built a production Scenario Update Agent that handles natural-language updates on nested time-series data. I’ve reduced end-to-end simulation latency by ~60–70% and improved decision turnaround by ~3×, while also delivering a RAG conversational platform for 5,000+ student queries using semantic retrieval (OpenAI embeddings + Qdrant) and a video-to-knowledge pipeline powered by Whisper.

Experience

Work history, roles, and key accomplishments

Current

AI Engineer - Scenario Agent

Current

Easy Hiring Infotech LLP

Nov 2025 - Present (5 months)

Built a production Scenario Update Agent that applies natural-language–driven updates to nested, multi-dimensional, and time-series data. Reduced end-to-end simulation latency by ~60–70% and improved decision turnaround by ~3× using automated agent execution and real-time recomputation with Redis-backed state.

Python LangGraph OpenAI Redis TOML Databricks

AI Engineer - RAG Platform

Easy Hiring Infotech LLP

Jan 2022 - Oct 2025 (3 years 9 months)

Developed and deployed a RAG-based conversational AI system handling 5,000+ student queries for 24/7 learning-path and placement guidance. Improved resume-job matching accuracy by ~50% and reduced manual screening effort by 3.1× through AI resume ingestion, semantic retrieval (OpenAI embeddings + Qdrant), and recommendation workflows.

Python RAG OpenAI Embeddings Qdrant fastAPI Streamlit PostgreSQL Whisper

AI Engineer - Computer Vision

NeoAstra

Jul 2021 - Jan 2022 (6 months)

Built AI models to automate cooking processes for the ID (Idly Dosa) client by detecting paratha shapes and textures to ensure preparation precision. Increased engagement by 30% for the AHA client via a personalized recommendation engine, and developed an accessory recommendation system analyzing stopped movie frames for real-time suggestions on JIO Cinema.

Python Machine Learning Deep Learning OpenCV Flask Recommendation Systems Computer Vision Personalization

Computer Vision Engineer

Dexler Energy

Jun 2020 - Jul 2021 (1 year 1 month)

Designed a custom OCR system to accurately extract captchas from noisy backgrounds, reducing manual data entry errors by 35%. Developed models to detect solar water heaters and calculate rooftop solar capacity to streamline solar energy planning.

OpenCV Python Selenium Flask Deep Learning Solar Capacity Estimation Computer Vision

Machine Learning Engineer

Tensorgo Technologies

Sep 2019 - May 2020 (8 months)

Automated data processing pipelines using Python, Pandas, NumPy, and scikit-learn to improve data handling and workflow efficiency. Produced data visualizations and reports that enabled senior management to make data-driven strategic decisions.

Python Pandas NumPy scikit learn Data Pipelines Data Analysis Web Scraping Data Labeling