Primary Responsibilities
- Design and develop Generative AI solutions using industry-standard frameworks, tools, and best practices
- Implement core components of GenAI applications, including prompt engineering, retrieval systems, and application integration
- Build and optimize RAG (Retrieval Augmented Generation) implementations for enterprise use cases
- Create reusable components and libraries to accelerate GenAI development across projects
- Conduct code reviews and ensure adherence to coding standards and architectural guidelines
- Troubleshoot and resolve technical issues throughout the development lifecycle
- Collaborate with data engineers and MLOps specialists to integrate solutions into production environments
- Document technical implementations, architectures, and development processes
- Participate in technical discussions with clients to gather requirements and provide implementation insights
- Stay current with emerging GenAI technologies and contribute to internal knowledge sharing
Required Qualifications
- 5+ years of software development experience with at least 2+ years focused on AI/ML technologies
- Strong programming skills in languages commonly used for AI development (Python)
- Hands-on experience with GenAI technologies including LLMs, transformer models, and embedding techniques
- Experience with at least one major AI platform or framework (e.g., OpenAI, Hugging Face, Azure OpenAI, AWS Bedrock)
- Knowledge of vector databases and similarity search algorithms for RAG implementations
- Understanding of software development best practices, including version control, testing, and CI/CD
- Experience building APIs and integrating with third-party services and enterprise systems
- Ability to work in an agile development environment with cross-functional teams
- Strong problem-solving skills and attention to detail
- Bachelor's degree in Computer Science, Engineering, or related technical field
Preferred Skills
- Experience implementing production-grade GenAI applications for enterprise clients
- Knowledge of prompt engineering techniques and optimization for different LLM models
- Familiarity with fine-tuning approaches for language models
- Experience with front-end frameworks for building AI-enabled user interfaces
- Understanding of data privacy and security considerations in AI implementations
- Knowledge of containerization and cloud deployment technologies
- Experience with MLOps tools and practices for AI solution deployment
- Exposure to different GenAI use cases across multiple industries