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Company Overview: RevGenie is at the forefront of AI-driven sales and marketing automation. Our mission is to build an advanced AI platform that utilizes cutting-edge machine learning algorithms to streamline and optimize lead generation, engagement, and conversion. We are creating a team of AI agents that perform large-scale data analysis, identify the right companies and contacts at the optimal time in their customer journey, and craft hyper-personalized email, LinkedIn, and call scripts. These AI agents continuously learn and improve, driving unmatched growth and efficiency for our clients. Why Join Us: At RevGenie, you’ll work on the bleeding edge of AI technology, developing tools that redefine the sales and marketing landscape. You’ll lead the design and deployment of AI systems using state-of-the-art libraries, frameworks, and tools, which empowers multiple AI agents to collaborate on complex tasks. If you’re passionate about AI and eager to push the boundaries of what’s possible in sales and marketing automation, this is the place for you. Job Summary: As the Lead AI/ML Engineer for the RevGenie AI Platform, you will be responsible for designing, developing, and deploying a suite of AI agents using cutting-edge libraries, frameworks, and models. These agents will conduct large-scale data analysis, target ideal companies and contacts, generate hyper-personalized engagement strategies, and continually improve through machine learning algorithms. You will leverage tools like Autogen/CrewAI, which enables seamless multi-agent collaboration, and integrate with various large language models (LLMs) for advanced NLP tasks. Responsibilities: ● Design and Development: Lead the design and implementation of AI agents capable of large-scale data analysis, customer journey mapping, and personalized content creation. ● ML/AI Frameworks: Utilize advanced AI/ML frameworks such as TensorFlow, PyTorch, and Scikit-learn to build and optimize machine learning models. ● Multi-Agent Collaboration: Use Augogen or CrewAI to orchestrate multiple AI agents, enabling them to work together efficiently on complex tasks. ● NLP and LLMs: Develop natural language processing models using libraries like Transformers by Hugging Face, integrating both open-source and commercial LLMs (e.G., GPT-4, IBM Granite, Cohere) for content generation and personalization. ● AutoML Integration: Implement AutoML solutions such as Augogen for automated model selection and hyperparameter tuning. ● MLOps and Deployment: Deploy AI models using tools like MLflow, TensorFlow Serving, and Kubeflow to ensure scalability and reliability in production environments. ● Continuous Improvement: Develop reinforcement learning algorithms and integrate with platforms like Autogen or CrewAI for continuous learning and real-time adaptation based on campaign outcomes and customer interactions. ● Data Handling and Processing: Leverage Pandas, NumPy, and Apache Spark for efficient data handling, preprocessing, and feature engineering. ● Collaboration: Work closely with cross-functional teams, including data scientists, software engineers, and sales experts, to align AI capabilities with business goals. ● Mentorship: Mentor junior engineers, set best practices for AI/ML development, and lead the adoption of new technologies within the team. Qualifications: ● Educational Background: Master’s or Ph.D. in Computer Science, Artificial Intelligence, Data Science, or a related field. ● Experience: 7 years in AI/ML development, with a strong focus on natural language processing (NLP), personalization algorithms, and data analysis. ● Programming Skills: Proficiency in Python, with extensive experience in AI/ML libraries such as TensorFlow, PyTorch, and Scikit-learn. ● NLP Expertise: Hands-on experience with NLP tools (e.G., spaCy, NLTK) and large language models, both open-source (e.G., GPT-4, LLaMA) and commercial (e.G., IBM Granite, Cohere). ● Multi-Agent Systems: Experience with platforms like Autogen or CrewAI, which enable the orchestration of multiple AI agents to work together on complex tasks. ● AutoML and MLOps: Experience with AutoML platforms (e.G., Augogen) and MLOps tools (e.G., MLflow, Kubeflow). ● Data Science Tools: Proficient in data manipulation and analysis using Pandas, NumPy, and large-scale data processing with Apache Spark. ● Cloud Platforms: Strong knowledge of cloud platforms (e.G., AWS, GCP, Azure) for deploying and managing AI models in production. ● Leadership: Proven track record of leading AI/ML initiatives, mentoring teams, and driving innovation in AI/ML projects. Preferred Qualifications: ● Reinforcement Learning: Experience with reinforcement learning frameworks (e.G., Stable-Baselines3) and their application in real-time decision-making. ● Graph ML: Familiarity with graph-based machine learning frameworks (e.G., DGL, PyTorch Geometric) for advanced customer segmentation and network analysis. ● Sales and Marketing Domain: Previous experience in the sales or marketing industry, particularly with CRM and marketing automation tools. ● Visualization Tools: Knowledge of data visualization libraries such as Matplotlib, Seaborn, and Plotly.