Generative AI Engineer

1.Role Definition:

Generative AI Solutions Lead responsible for developing and implementing AI-driven business solutions, fostering client relationships, and driving company growth through innovative AI technologies.

2. Key Responsibilities:

a. AI Technology Showcase and Solution Development
b. Team Leadership and AI Project Management
c. AI Business Development and Client Relationship Management
d. Cross-functional Collaboration in AI Integration
e. Contribution to Company's AI Strategy and Growth

3. Skills and Competencies:

a. Technical Expertise (Generative AI, Machine Learning, NLP)
b. AI Leadership and Project Management
c. AI Business Acumen and Client Engagement
d. Adaptability and Innovation in AI Applications
e. Communication of Complex AI Concepts

Detailed Technical Expertise

• Deep understanding of Generative AI models (e.g., GPT, BERT, T5)
• Proficiency in Retrieval-Augmented Generation (RAG) techniques
• Advanced Natural Language Processing (NLP) skills
• Experience with Agentic AI solutions and proof-of-concepts
• Expertise in prompt engineering and optimization
• Knowledge of AI model deployment and production environments
• Familiarity with popular AI frameworks (e.g., TensorFlow, PyTorch)
• Ability to keep up-to-date with rapidly evolving AI technologies
• Understanding of AI ethics and responsible AI practices

4. Performance Indicators:

a. AI Solution Quality and Innovation
b. AI Project Delivery Success
c. Client Satisfaction with AI Solutions
d. New AI Business Generation from Existing Clients
e. Contribution to Company's AI Capabilities and Efficiency

5. Growth Potential:

a. Ability to identify and pursue new AI business opportunities
b. Capacity to take on additional AI-related responsibilities in a small company
c. Potential to contribute to strategic AI decision-making and company direction

6. Project Experience:

The ideal candidate should have delivered projects such as:

a. Large-scale language models fine-tuned for specific business domains
b. RAG systems integrated with enterprise knowledge bases
c. Conversational AI agents for customer service or internal operations
d. AI-driven content generation systems for marketing or documentation
e. Predictive models for business forecasting or decision support
f. AI-powered automation solutions for business processes
g. Proof-of-concepts demonstrating cutting-edge AI capabilities
h. AI model optimization for improved performance and reduced costs
i. Integration of AI solutions with existing enterprise systems
j. Development of AI-enhanced products or services for clients