Ruli

Head of Marketing

Ocean Beach Community Foundation, Inc.

Elected Board Director

Portable

Growth & Business Development Manager

Strategic Business Partner

Chegg Inc.

Partnerships Marketing Manager

Strategic Partnerships Development Manager

Simba Dickie Group

Project Manager

Hilton

Marketing Consultant

Ex Meta | Data-Driven | Mentor

Passionate about partnerships and people, I'm a Marketing Leader with over a decade of experience across diverse industries, I thrive on building robust partnerships and creating innovative product integrations that lead to mutual growth. I've honed the ability to identify whitespace opportunities and bring together mutually interested parties to drive innovation.

𝐌𝐲 𝐉𝐨𝐮𝐫𝐧𝐞𝐲: Over the past 10+ years, I've been fortunate to contribute to the implementation of growth initiatives and strategic alliances, shaping the trajectory of businesses. My genuine excitement comes from identifying market gaps and crafting high-impact partnerships that leave all sides better off.

𝐊𝐞𝐲 𝐒𝐭𝐫𝐞𝐧𝐠𝐭𝐡𝐬: I excel in relationship-building, strategizing impactful partnerships, and enhancing digital operations. By closely listening to partners, understanding their needs, and collaborating effectively, I've consistently achieved shared goals. 

𝐎𝐩𝐞𝐧 𝐟𝐨𝐫 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧𝐬: Eager to apply my skills and experience to new challenges, I'm here to drive success for my partners. Let's connect and explore the potential opportunities ahead.

Currently, I am focused on leveraging my partnership talents to scale ethical AI adoption globally. With experience across regions, I see tremendous potential but also risks from rapid innovation without responsibility. Mindful commercialization is key.

Outside of partnerships, you'll find me on the beach with my pups or planning my next ski trip. If you share a passion for animals or human-centric AI, I would love to exchange perspectives!

𝗣.𝗦. Here's what coworkers say about me:

"𝘙𝘰𝘶𝘨𝘦 𝘪𝘴 𝘢 𝘴𝘵𝘢𝘯𝘥𝘰𝘶𝘵 𝘵𝘢𝘭𝘦𝘯𝘵 𝘸𝘩𝘦𝘯 𝘪𝘵 𝘤𝘰𝘮𝘦𝘴 𝘵𝘰 𝘣𝘶𝘪𝘭𝘥𝘪𝘯𝘨 𝘱𝘢𝘳𝘵𝘯𝘦𝘳𝘴𝘩𝘪𝘱𝘴. 𝘏𝘦𝘳 𝘴𝘵𝘳𝘢𝘵𝘦𝘨𝘪𝘤 𝘮𝘪𝘯𝘥𝘴𝘦𝘵 𝘢𝘯𝘥 𝘦𝘹𝘤𝘦𝘭𝘭𝘦𝘯𝘵 𝘤𝘰𝘮𝘮𝘶𝘯𝘪𝘤𝘢𝘵𝘪𝘰𝘯 𝘴𝘬𝘪𝘭𝘭𝘴 𝘤𝘰𝘯𝘴𝘪𝘴𝘵𝘦𝘯𝘵𝘭𝘺 𝘥𝘳𝘪𝘷𝘦 𝘪𝘮𝘱𝘢𝘤𝘵𝘧𝘶𝘭 𝘤𝘰𝘭𝘭𝘢𝘣𝘰𝘳𝘢𝘵𝘪𝘰𝘯𝘴. 𝘞𝘰𝘳𝘬𝘪𝘯𝘨 𝘸𝘪𝘵𝘩 𝘙𝘰𝘶𝘨𝘦 𝘩𝘢𝘴 𝘣𝘦𝘦𝘯 𝘢 𝘱𝘭𝘦𝘢𝘴𝘶𝘳𝘦, 𝘢𝘯𝘥 𝘩𝘦𝘳 𝘥𝘦𝘥𝘪𝘤𝘢𝘵𝘪𝘰𝘯 𝘵𝘰 𝘧𝘰𝘴𝘵𝘦𝘳𝘪𝘯𝘨 𝘪𝘯𝘯𝘰𝘷𝘢𝘵𝘪𝘰𝘯 𝘵𝘩𝘳𝘰𝘶𝘨𝘩 𝘱𝘢𝘳𝘵𝘯𝘦𝘳𝘴𝘩𝘪𝘱𝘴 𝘪𝘴 𝘵𝘳𝘶𝘭𝘺 𝘤𝘰𝘮𝘮𝘦𝘯𝘥𝘢𝘣𝘭𝘦."
- 𝘑𝘢𝘮𝘦𝘴 𝘚𝘭𝘦𝘦𝘮𝘢𝘯

Rouge Y.

🚀 We’re hiring!

Ruli is growing, and I’m looking for a Marketing Associate to join our team.
If you’re passionate about AI, storytelling, and the future of legal tech, this is your chance to help build something special. You’ll work closely with me on events, content, and GTM programs that are helping shape how in-house legal teams adopt AI.

We move fast, think creatively, and have fun along the way.

💡 Location: Remote (US-based preferred)
 💼 Experience: 1–3 years in marketing, events, or content
 📩 Apply here: https://lnkd.in/g3Vjwk5A

Excited to meet someone who’s ready to grow, experiment, and make real impact.

#MarketingJobs #AI #LegalTech #Hiring #StartupLife #RuliAI

Senior Growth Leader | Ex-Meta | Elected Board Director | AI Thought Leader

Do the Data

Founder

Deloitte

Data Analytics Manager

Novartis

Lead a team of data analysts and oversee the design and implementation of healthcare data analytics projects

Develop and maintain healthcare data management systems, ensuring data accuracy, completeness, and security

Develop and implement data analysis plans to support clinical and operational decision-making processes

Collaborate with stakeholders across the organization to identify and prioritize business intelligence needs, and ensure that data analytics projects align with the overall strategic goals of the organization
Analyze large datasets using statistical and data visualization tools to identify trends, patterns, and insights that can be used to drive business performance

Ensure compliance with data privacy and security regulations such as HIPAA, and work with legal and compliance teams to address any data-related issues

Develop and deliver presentations to communicate complex data analysis to non-technical stakeholders, including executive leadership, and make recommendations for process improvements based on data-driven insights

Stay up-to-date with the latest trends and technologies in healthcare data analytics and provide guidance on how the organization can leverage them to improve performance and patient outcomes

Data Analytics - Turning Coffee into Insights, One Caffeine-Fueled Query at a Time! | Healthcare Data | Financial Expert | Driving Business Growth | Data Science Consultant | Data Strategy

As a data analytics professional, I have a passion for turning raw data into meaningful insights that drive business decisions. With my experience in data visualization, statistical analysis, and data modeling, I can help your organization harness the power of data to optimize operations, improve customer experiences, and achieve your strategic goals.

🔍 Discovering Insights

My expertise lies in exploring complex data sets, uncovering hidden trends and patterns, and providing actionable insights that support informed decision-making. I can help you answer questions like "What are our customer's buying habits?" or "What factors are impacting our sales performance?", and use data to guide your business strategy.

🎨 Data Storytelling

I believe that data storytelling is an art form, and I love using creative and engaging data visualizations to communicate complex information in a way that's easy to understand. Whether it's through interactive dashboards or custom reports, I can help you see your data in a new light, and use it to tell the story of your business.

🧠 Continuous Learning

I'm a lifelong learner, and I'm always exploring new data tools and techniques to stay at the forefront of the industry. Whether it's machine learning, artificial intelligence, or predictive analytics, I'm excited to apply the latest advancements to help you gain a competitive edge.

🤝 Let's Connect

If you're looking for a data analytics professional who can help you unlock the power of your data, let's connect! Whether you're a startup or a large corporation, I'm excited to collaborate with you and help you achieve your business goals. Feel free to reach out at Ravenaravs@gmail.com

Ravena O

Python for Data Professionals: Why It’s the Preferred Choice

📍 Why is Python the go-to language for data roles?

Python isn’t just a programming language—it’s an essential tool for data professionals. Its simplicity, versatility, and vast ecosystem of libraries make it a top choice for handling complex data challenges with ease.

📍 Where does Python fit in the data ecosystem?

🔴 Data ingestion, cleaning, and transformation
🔴 Storage and orchestration
🔴 Visualization and reporting
🔴 Machine learning and model deployment

From building ETL pipelines to creating interactive dashboards, Python empowers data teams at every level.

📍 Why choose Python over Scala/Java?

While Scala and Java are excellent for large-scale data processing and system robustness, Python stands out for its flexibility. It’s a universal tool, adaptable across various data domains, making it the preferred choice for many professionals.
📍 Key data roles where Python is invaluable:
🔴 Data Analytics – Analyze data using Pandas, NumPy
🔴 Data Visualization – Create stunning visuals with Seaborn, Matplotlib, Plotly
🔴 Machine Learning – Build models with Scikit-learn, TensorFlow, Keras
🔴 Web Scraping – Extract data using Scrapy, BeautifulSoup, Selenium
🔴 Data Engineering – Implement pipelines with Spark, Kafka, Flink
🔴 Databases – Work with SQLite, PostgreSQL, MongoDB, MySQL

📍 Top free platforms to upskill in Python:
 - realpython.com
- pythonprogramming.net
- datacamp.com (free basic courses)
- w3schools.com/python
- https://lnkd.in/gCDhJvA2
- https://lnkd.in/grqYWfGW

 📍 Follow these Python experts for insights: 🐍

Matt Harrison, Khuyen Tran, Guido van Rossum, Dan Bader

Python remains an essential skill for data professionals—whether you're analyzing, engineering, or visualizing data, it has the tools to get the job done!

CC:Brij Kishore Pandey

Ravena  O

Ever wondered how companies actually build AI systems powered by LLMs?

It’s not just about calling an API and hoping for magic.

There’s an entire tech stack behind the scenes — and once you understand it, you’ll see how everything connects.

Let’s break it down step by step 👇

🟡 Step 1: The Brain (LLMs)
 Think of GPT, Gemini, or Claude as the intelligence layer.
 They understand, reason, and respond — but they need context, data, and direction.

🟡 Step 2: The Connectors (Frameworks)
 Frameworks like LangChain or LlamaIndex act as plumbing between your model and your tools.
 They let you combine the LLM with data sources, APIs, and logic — no need to reinvent the wheel.

🟡 Step 3: The Memory (Vector Databases)
 LLMs forget fast.
 That’s where databases like Pinecone, Weaviate, and Chroma come in — they store “embeddings” (a fancy word for text turned into numbers).
 It’s like giving your AI Google Search for your own data.

🟡 Step 4: The Fuel (Data Extraction)
 No data, no intelligence.
 Tools like Crawl4AI and FireCrawl pull information from PDFs, websites, and documents — then clean it so your AI can actually use it.

🟡 Step 5: The Open Source Edge
 Don’t want to rely on expensive APIs?
 Use open models from Hugging Face or run local ones via Ollama.
 Freedom + privacy + control = ✅

🟡 Step 6: The Language of Numbers (Embeddings)
 Before your text hits the vector DB, it must be converted into numerical form.
 That’s where OpenAI Embeddings or SBERT come in — enabling true semantic understanding.

🟡 Step 7: The Quality Gate (Evaluation)
Now test it.
Use tools like Giskard or Trulens to check for accuracy, hallucinations, and relevance.
Because every smart system needs smarter feedback loops.

💥 In short:
 1️⃣ Pick your LLM
 2️⃣ Connect it via a framework
 3️⃣ Feed it structured data
 4️⃣ Store it in a vector DB
 5️⃣ Add embeddings for meaning
 6️⃣ Use open tools for flexibility
 7️⃣ Evaluate relentlessly

This blueprint can help your team go from idea to intelligent system.

 🔁 Repost if you want your network to build smarter with AI.

CC: Rahul Agarwal

Ever wondered how Agentic AI actually works behind the scenes?

Here’s a simple breakdown of how an AI agent transforms raw input → into intelligent output.

 Perfect for anyone curious about how autonomous AI systems really think. 

🟠 Input Sources
 Agentic AI starts by collecting data from multiple channels — knowledge bases, user queries, APIs, logs, and web data.

🟠  AI Processing
Once data is gathered, the agent gets to work — analyzing the query, reasoning through context, retrieving memory, planning tasks, selecting tools, and managing the entire flow.

🟠 3. Action Layer
 With a clear understanding of context, the agent performs real-world actions — making decisions, executing tasks, fixing errors, collaborating with other agents, and even scheduling future steps.

🟠 4. Output
 Finally, it delivers a response that’s accurate, relevant, and context-aware — the hallmark of truly autonomous intelligence.
That’s what sets Agentic AI apart from traditional chatbots or rule-based systems.

 💾 Save this post for your next deep dive into AI systems!

CC: Prem Natarajan

freeCodeCamp  recently released a comprehensive AI Engineering Roadmap — and it’s surprising how many people missed it.

It’s not just another tutorial list.
It’s a reality check for anyone serious about building a career in AI.

Here are some key takeaways worth reflecting on 👇


🔴 1. Math is non-negotiable
Linear algebra, calculus, and probability form the backbone of every AI system.
If you can’t understand gradients, optimization, and loss functions — you’ll struggle to build anything meaningful.

🔴 2. Python remains the foundation
From TensorFlow and PyTorch to NumPy and Pandas — this ecosystem drives modern AI.
Fluency in Python isn’t optional; it’s expected.

🔴 3. LLMs aren’t everything
While everyone is chasing ChatGPT replicas, real impact (and opportunity) lies in building domain-specific models that solve practical business problems.

🔴 4. Depth beats credentials
Building and deploying CNNs, RNNs, and LSTMs will teach you far more than any certificate ever could.

🔴 5. Data work is underrated
Data cleaning, transformation, and validation consume most of an AI engineer’s time — and mastering these makes you truly valuable.

🔴 6. Prompt engineering isn’t the destination
It’s a useful skill — but true AI engineers design systems, not just prompts.

🔴 7. Deployment is the differentiator
A model sitting in a Jupyter notebook has no value.
Productionization is where engineering begins.

🔴 8. Generative AI goes beyond LLMs
Understanding GANs, VAEs, and foundational architectures separates professionals from hobbyists.

🔴 9. System design matters
AI solutions must scale. Knowing Kubernetes, Docker, and distributed systems is critical for real-world impact.

🔴 10. Ethics and bias awareness are essential
As AI adoption accelerates, responsible design and fairness aren’t optional — they’re requirements for trust and compliance.



In short:
The roadmap reminds us that AI engineering isn’t just about chasing trends — it’s about mastering fundamentals, building production-ready systems, and thinking responsibly about the technology create

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The Battle

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Ravena O

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The Battle

Rouge Y.

Ravena O

Followers

Posts

Engagements

Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Rouge Y.'s LinkedIn Performance

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