Manasi Swaminathan

Founding AI Engineer at Ren

Generative AILarge Language Models (LLMs)AI AgentsHuman-Computer Interaction (HCI)Natural Language Processing (NLP)RAG (Retrieval-Augmented Generation)

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About

I'm Manasi Swaminathan, the Founding AI Engineer at Ren. My work sits at the intersection of deep technical AI engineering and Human-Computer Interaction, focusing on how we can build systems that truly coexist with and support humans. Throughout my career—from researching social robotics at Indiana University to scaling Generative AI products at FourKites—I've been driven by the goal of making technology more human-centered. I'm particularly passionate about using AI to support well-being and 'Ikigai,' and I believe that the best AI systems are built with restraint and a deep understanding of user psychology. I love connecting with the AI research community and industry leaders to discuss the future of conversational agents and ethical data science. Whether it's optimizing LLM throughput or designing robots for dementia care, I'm always looking for ways to blend technical rigor with meaningful human impact.

Networking

What I can offer

›Expertise in RAG and AI Agent architecture
›Deep technical AI engineering combined with UX/HCI research
›Insights into human-centered AI design

Looking for

›expanding my professional network
›exploring mutual opportunities in AI research and industry leadership

Best fit for

AI research communityIndustry leaders in management and coachingAspiring data scientists

Current Interests

Social RoboticsAI CoachingHuman-Centered AIIkigai and well-beingSmart Cities

Background

Career

Transitioned from research roles at Swinburne and Indiana University into data science and AI engineering, moving from a Data Science Intern at FourKites to a Founding AI Engineer role at Ren.

Education

Master of Science (MS) in Computer Science from Indiana University Bloomington

Achievements

›Spearheaded GenAI research at FourKites, scaling a product to 380+ recurring users.
›Increased LLM development throughput by 30% and identified a $10M+ monetization opportunity.
›Reduced negative AI sampling feedback by 74% through improved QA design.
›Developed 'I.R.I.S' for older adults, featured in WIRED and NBC News.
›Co-authored three papers for the ACM/IEEE International Conference on Human-Robot Interaction.

Opinions

Effective AI coaching requires careful system design, iteration, and restraint.
Robots should coexist with humans rather than just responding to them.
The next generation of data scientists must possess interdisciplinary skills and a deep understanding of ethics and privacy.