Abhijit Dengale

Mastering AI-native engineering full-time, specializing in agentic systems, LLM tooling, multi-agent frameworks, and autonomous workflow design via Cursor, Windsurf, LangChain, and OpenAI APIs.

Conducting advanced research into RAG optimization, AI code generation, and LLM fine-tuning pipelines to develop production-grade agentic architectures.

Engineered intelligent automation systems automating job matching, candidate screening, and application workflows, cutting recruitment time by 70% via resume parsing and RAG-based role matching.

Created workflow integrations across third-party job boards, email systems, and analytics dashboards, streamlining recruitment processes for enhanced efficiency.

Shipped full-stack features (React, Node.js, Supabase) supporting 10K+ remote jobs across 500+ companies and 50+ countries.

Architected and deployed 15+ production AI-native systems, integrating LLMs, agentic workflows, RAG pipelines, and computer vision, driving 40% client acquisition growth.

Designed multi-agent orchestration frameworks (LangChain, OpenAI) and AI chatbots with multi-turn context and sentiment analysis, improving user engagement by 40%.

Implemented MLOps pipelines with model monitoring, drift detection, and retraining on AWS & GCP, ensuring system robustness and optimal performance.

Led a cross-functional team of 11+ engineers, reducing project delivery timelines by 15% via Agile/Scrum methodologies.

Managed 15+ developers across 8 fintech projects valued at over $2M, successfully integrating ML models into critical financial data pipelines.

Developed AI-driven financial forecasting tools, achieving 92% accuracy in stock trend predictions and improving delivery efficiency by 20%.

Built cross-platform apps (Flutter, ReactJS) with AI-driven personalization and NLP chatbots, cutting query resolution time by 50% and support tickets by 30%.

Optimized FastAPI/Node.js backends, reducing average load times by 30% and enhancing system performance.