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Generative AI Engineer with 3+ years in Generative AI & NLP
AI/ML Engineer with 2.9+ years of hands-on experience designing, developing, and deploying state-of-the-art generative AI models and NLP-driven solutions. Deep expertise in Generative AI model development, LLM fine-tuning and optimisation, advanced NLP techniques (text generation, summarisation, sentiment analysis, translation), and deep learning. Proficient in Python with frameworks including Hugging Face, OpenAI GPT, LangChain, AutoGen, spaCy, PyTorch, and TensorFlow. Experienced in AI model deployment and MLOps using FastAPI, Docker, Kubernetes, and CI/CD pipelines across AWS, GCP, and Azure. Skilled in conversational AI and chatbot development, data engineering with Apache Spark and Pandas, and delivering domain-specific AI solutions tailored to real-world business needs.
Imartius Learning Pvt. Ltd.
Post Graduate Diploma · Data Analytics & Machine Learning
March 1, 2022 – December 1, 2022
Walchand Institute of Technology, Solapur
Bachelor of Engineering (B.E.)
June 1, 2015 – June 1, 2018
Rudrastar Impex Pvt. Ltd.
AI/ML Engineer
May 1, 2024 – Present
Pune, Maharashtra, India
The Strelema
Python Developer (AI/ML)
April 1, 2023 – April 1, 2024
Pune, Maharashtra, India
AgriSense AI – Generative AI Model + NLP + Computer Vision Production Platform
September 1, 2025 – January 1, 2026
Designed and deployed a state-of-the-art generative AI model application – fine-tuned LLaMA/GPT-4 on domain-specific agricultural data using Hugging Face; built RAG pipeline (LangChain + FAISS) for knowledge-grounded NLP text generation and context-aware advisory. Applied advanced NLP techniques (text generation, summarisation, entity extraction via spaCy) and deep learning (CNN/TensorFlow – 90%+ accuracy, 20K+ images); optimised model performance and scalability for production inference. Deployed via FastAPI with Docker containerisation and CI/CD (GitHub Actions) on AWS (EC2, ECR, SageMaker); implemented model monitoring and observability for production reliability.
AI Sentiment Analysis System - NLP & LLM-Powered Generative AI Application
May 1, 2024 – August 1, 2025
Developed and fine-tuned generative AI models (LLaMA, Mistral – Hugging Face) for real-time NLP tasks – sentiment analysis, text classification, and summarisation on large-scale political text data using Apache Spark for distributed data processing. Implemented advanced NLP techniques (tokenisation, NER, entity resolution via spaCy/NLTK) and conversational AI components (LangChain, AutoGen) for context-aware, domain-specific insights. Built scalable model deployment pipeline; developed Streamlit dashboard for real-time NLP analytics and automated stakeholder reporting.
Python Automation & ML Data Engineering Platform
April 1, 2023 – April 1, 2024
Built ML models and NLP-driven data engineering pipelines for automated text processing and pattern recognition; integrated into FastAPI REST services with clean OOP Python code – reducing effort by ~60%. Applied statistical analysis and feature engineering for training data quality improvement; delivered actionable visualisation insights for business decision-making.
Cultural Fit Analysis
The candidate demonstrates a strong cultural fit for a Generative AI Engineer role due to their deep specialization in generative AI, NLP, and MLOps. The diversity of projects, ranging from agricultural AI to sentiment analysis and data engineering, showcases adaptability and a broad application of AI skills. The continuous learning indicated by the Post Graduate Diploma in Data Analytics & Machine Learning, coupled with hands-on experience with cutting-edge frameworks and models, aligns well with an innovative and growth-oriented culture.
Soft Skills & Operational Fit
The candidate's project descriptions and professional experience indicate a strong ability to collaborate with cross-functional teams, deliver data-driven insights, and maintain code quality. The focus on production deployment, monitoring, and scalability suggests an operational mindset. The detailed descriptions of problem-solving (e.g., reducing hallucinations by ~35%) highlight a results-oriented approach.