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Sovik K. K. Nath
Generative AI and AI/ML Solution Architect Lead - AWS | Capital Markets | Financial Services
Sovik K. Nath is a seasoned Senior Solutions Architect, digital strategist, and AI/ML professional with a deep expertise in Generative AI. With 14 years of experience, he has demonstrated consistent growth in driving business outcomes through problem-solving, AIML, design thinking, and digital solution conceptualization.
His proficiency lies in leveraging data-driven insights, advanced analytics, and machine learning techniques to provide strategic solutions within sectors like financial services, capital markets, operational analytics, marketing analytics, supply chain, predictive maintenance, and the internet-of-things.
Sovik excels in stakeholder management, business requirements identification, technical analysis, and engagement design and delivery. His technical skill set includes Generative AI, Python, R Studio, cloud computing platforms like AWS and Azure, big data ecosystem tools, Tableau, Rapid Miner, and Microsoft data technologies.
Education-wise, Sovik holds a Master of Science in Industrial Engineering from the University of South Florida, a Master of Science in Geological and Earth Sciences from Université de Fribourg, and a Bachelor's degree in Architecture from Indian Institute of Technology, Kharagpur. Additionally, he has pursued certificates in Business Analytics from The Wharton School and Executive Data Science from The Johns Hopkins University.
In his professional journey, Sovik has held key roles at renowned organizations such as Amazon Web Services (AWS), Johnson & Johnson, Mareana Inc., Slalom, Columbia University, Honeywell, among others. His diverse experience includes serving as an AI/ML Senior Specialist Solution Architect, AIML Architect, Technology Architect, Consultant, Data Scientist, and Graduate Research Assistant.
Through his career, Sovik has honed his skills in strategic design thinking, technology enablement, and delivering impactful solutions through AI, ML, and analytics. His expertise in Generative AI, financial services, and stakeholder management sets him apart as a dynamic professional in the field.
Highlights
Ongoing series - Generative AI and AI/ML for Financial Services and Capital Markets.
✅ Week 1 - Generative AI use cases in Capital Markets ● Details about potential high-impact business use cases Section : "AI/ML and Generative AI Use Cases in Capital Markets Value Chain" White-paper - https://go.aws/3SonSi0 https://lnkd.in/g9vrtRTH
✅ Week 2 - Generative AI Strategy ● Details in sections "Considerations for Designing Generative AI Strategy" and "AWS Generative AI Offerings" White-paper - https://go.aws/3SonSi0 https://lnkd.in/gy5aB4k3
✅ Week 3 - Prompt Engineering Techniques ● Details about advanced prompt engineering for financial statement analysis, hypothesis testing and cause-effect analysis. Blog Link : https://lnkd.in/gUnjftHw Github Code : https://lnkd.in/gWQECPcp https://lnkd.in/gPB6vDht
▶ Week 4 - Retrieval Augmented Generation (RAG) and Agents ⏺ Week 5 - Fine-tuning ⏺ Week 6 - Path to Production
Let's continue : 📖
▶4️⃣ Week 4 - Retrieval Augmented Generation (RAG) and Agents
AI-powered assistants for investment research with multi-modal data: An application of Amazon Bedrock Agents
✴ Find out how financial analysts can use various data sources of multifaceted financial data (text, audio, and databases) and various tools (detect phrases, portfolio optimization, sentiment analysis, and stock query) to gather financial insights. ● Learn about benefits and lessons learned in migrating from LangChain agents to Amazon Bedrock Agents
Blog link : https://lnkd.in/gQ-TaP2i Code link : https://lnkd.in/gh8bKJ_2
#aws #amazon #capitalmarkets #generativeai #RAG #agents #bedrock #anthropic
Over the next few weeks, every week I will share one of the contents as a part of this ongoing series (Generative AI and AI/ML for Financial Services and Capital Markets) on the following topics.
✅ Week 1 - Generative AI use cases in Capital Markets ● Details about potential high-impact business use cases Section : "AI/ML and Generative AI Use Cases in Capital Markets Value Chain" White-paper - https://go.aws/3SonSi0
https://lnkd.in/g9vrtRTH
▶ Week 2 - Generative AI Strategy ⏺ Week 3 - Prompt Engineering Techniques ⏺ Week 4 - Retrieval Augmented Generation (RAG) and Agents ⏺ Week 5 - Fine-tuning ⏺ Week 6 - Path to Production
Let's continue : 📖
▶2️⃣ Week 2 - Generative AI and AI/ML Strategy
✴ End-to-end Data Lifecycle - Unify data from disparate sources, both structured and unstructured data ✴ Unique Personas - Adoption of generative AI technology is driven by diverse personas with distinct goals and expectations ✴ Generative AI Investment Strategy - Depends how you construct your generative AI solution, either by building, buying, or hybrid.
Catch details in the white-paper sections ● "Considerations for Designing Generative AI Strategy" ● "AWS Generative AI Offerings" ● NIMIT JAIN
https://go.aws/3SonSi0
#aws #amazon #capitalmarkets #generativeai #strategy #sagemaker #bedrock #aiml
