Prakalp Somawanshi

Prakalp Somawanshi

Principal AI Engineer

Shell

Prakalp Somawanshi, currently a Principal AI Engineer at Shell Technology Center, holds a Bachelor's in Instrumentation and Control from the University of Pune and a Master's in Control & Computing from IIT Bombay. He gained valuable experience at Computational Research Laboratories, Pune, focusing on cryptography and high-performance computing after his master's. With nearly a decade at Shell, he's contributed extensively to diverse areas like geophysical algorithms, machine learning, machine vision, and reservoir modeling. In his role as a Principal AI Engineer, Prakalp primarily contributes to developing solutions in the realms of IoT and edge technologies, while also spearheading the creation of advanced edge compute capabilities.

As energy infrastructure evolves with the integration of renewables and digital operations, the need for intelligent and automated inspection systems is more critical than ever. This session explores how Multi-Modal Generative AI-combining vision and language models-can be applied to transform raw inspection data (images + text) into structured, actionable maintenance reports. 

Participants will learn to build a GenAI-powered inspection assistant that analyzes images (e.g., solar panel defects, pipeline anomalies) and corresponding technician notes to generate human-readable reports. The session bridges computer vision, natural language processing, and domain-specific prompts to automate tasks traditionally done by expert operators, thus enhancing safety, efficiency, and compliance in the energy sector.

This is a hands-on session with synthetic data and open-source tools to empower participants to prototype and deploy multi-modal GenAI solutions

Key Technologies & Tools

  • Image Captioning: BLIP, MiniGPT-4, Gemini Vision API, or HuggingFace Vision-Language models 
  • LLM Prompting: OpenAI GPT-4 / LLama 3 + LangChain 
  • Vector Store (optional): FAISS or Chroma if incorporating image metadata or prior records 
  • Frontend UI: Streamlit or Gradio 
  • Data Format: Drone/CCTV image samples + text logs in CSV/JSON

Read More

Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More

Managing and scaling ML workloads have never been a bigger challenge in the past. Data scientists are looking for collaboration, building, training, and re-iterating thousands of AI experiments. On the flip side ML engineers are looking for distributed training, artifact management, and automated deployment for high performance

Read More
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