Generative Ai Engineer, Ai Seed Stage Startup

• Conduct research on each individual

• Create hyper-relevant sequences

• Answer prospect’s questions, comments, and concerns

• Offer times

• Book meetings

GenAI Generalist Engineer:

Work will involve building semantic search engines, LLM fine-tuning, Vector DB applications, and cutting-edge data science work. We are looking for a strong builder with a track record of building generative AI projects and a desire to learn and iterate quickly.

Description:

GenAI engineer with deep LLM experience to join an early Stage Startup team and help build Generative AI powered solutions. Build and maintain infra to process thousands of generations per month from LLM calls. Design and implement novel AI prompts. Work closely with the CEO, CTO/Head of ML, and other Founding Engineers.

Have complete ownership over the work you do — as a founding team member, you’ll have the opportunity to truly own large areas of product and engineering and have direct relationships with customers.

Work directly with the CEO, CTO, and other founding members and help build out our team.

Industry experience + strong fundamentals + product mindset needed, owns modules, growth mindset, enjoys building, purpose-driven organization.

Key Responsibilities:

• Design and implement novel prompts and techniques to coax the best outputs from different LLMs. We are building a new territory of enterprise AI that previously did not exist. It’s up to us to discover and implement the best techniques that work for our users.
• Innovate with the latest models and advancements in Generative AI to determine the best model for a given use case.
• Have complete ownership over the work you do — as a founding team member, you’ll have the opportunity to truly own large areas of product and engineering and have direct relationship with customers.
• Validate prompts, try to break them, figure out ways in which a model won’t work. Manage datasets and prompts.
• Apply advanced prompt engineering techniques, insert guard rails, scale the application with Google/AWS cloud.
• Understand business problems, making sense of the data landscape & footprint, performing a combination of Gen AI, Advanced NLP, Exploratory analysis
• Develop generative solutions, optimize models, familiarity with frameworks like Langchain, Llama index, LLM open-source models including LLAMA and related frameworks, and proficiency in Python.
• Assist with developing the context-aware chatbots
• Build, and maintain, durable, scalable infrastructure capable of processing calls to LLMs and the reliability of the output.

What We're Looking For:

• Experience in AI and research with a detailed understanding of AI and its
• application in industry.
• Strong record of accomplishment in building highly scalable AI capabilities.
• Advanced communication skills across a variety of audiences; ability to make the complex simple, and to win mindshare through storytelling and dialogue.
• Prior experience with research and the industrialisation of AI into products.
• Strong business and financial acumen; ability to conceptualize, prioritize, and manage environments; ability to deliver high-volume applications with an eye for user experience.
• Proficiency in analyzing large datasets and proposing action plans.
• Ability to identify patterns and trends in data.
• Strong analytical mindset with a passion for data-driven decisions.
• Ability to present data creatively and tell a compelling story through insights.
• Aptitude for data and basic SQL database skills.

1. Data Collection

• Web Tracking: Using cookies and web beacons to track user behavior across websites. This can reveal which products or services a user is researching.
• IP Identification: Identifying and tracking the IP addresses of visitors to understand which companies are showing interest in specific topics.
• Content Syndication Networks: Gathering data from networks where companies distribute content to capture leads and gauge interest levels in specific topics.

Technologies Used:

• JavaScript trackers on web pages
• Web analytics platforms
• Content management systems (CMS) with tracking capabilities

2. Data Aggregation and Enrichment

• Data Integration: Combining data from various sources to build a complete profile.
• Data Enrichment: Adding information from external databases to enhance the understanding of each lead, such as industry data, company size, and other relevant attributes.

Technologies Used:

• Data lakes and warehouses for storing and managing data.
• ETL (Extract, Transform, Load) tools for data integration.
• Third-party data providers for enrichment.

3. Intent Signal Analysis

• Logistic Regression, Decision Trees and Random Forests, Support Vector Machines (SVM), KNN, Clustering Algorithms (e.g., K-Means, Hierarchical Clustering)
• Natural Language Processing (NLP): To understand the context and sentiment behind content engagement and social media interactions.

Technologies Used:

• Machine learning frameworks like TensorFlow or PyTorch.
• NLP libraries such as NLTK or SpaCy for processing text data.

4. Predictive Modeling

• Predictive Analytics: Utilizing statistical models and machine learning to predict future behaviors based on past actions and intent signals.

Technologies Used:

• Predictive modeling tools and platforms.
• Custom-built algorithms developed by data scientists.

5. Integration and Activation

• APIs and Integrations: To feed the intent data and insights directly into CRM and marketing automation platforms for personalized marketing campaigns and sales outreach.
• Real-time Data Processing: To ensure that sales and marketing teams can act on the insights in a timely manner.