Generative AI Engineer

Position Summary

The Senior Generative AI Developer will be a key contributor responsible for the design, development, testing, and deployment of production-grade Generative AI applications. This hands-on role requires deep expertise in modern software development practices, proficiency in Large Language Models (LLMs), and the ability to integrate sophisticated AI capabilities into existing enterprise software systems. The ideal candidate will mentor junior team members and drive technical excellence in GenAI implementation.

Key Responsibilities and Duties

Generative AI Development and Implementation

Code Development: Write clean, efficient, and well-documented code primarily in Python to build Generative AI features, microservices, and APIs.

Prompt Engineering: Design, implement, and rigorously test advanced Prompt Engineering techniques (e.g., Chain-of-Thought, few-shot learning) to maximize model performance and adherence to task specifications.

RAG System Development: Architect and implement high-performance Retrieval-Augmented Generation (RAG) pipelines, including data chunking, embedding generation, vector database interaction (e.g., Pinecone, ChromaDB), and context retrieval optimization.

Model Integration: Integrate, customize, and deploy both commercial (OpenAI, Anthropic) and open-source (Llama, Mistral) LLMs via APIs, local inference, or cloud-based services (e.g., AWS Bedrock, Azure OpenAI).

Model Fine-Tuning (LoRA): Execute model fine-tuning and adaptation techniques, such as LoRA (Low-Rank Adaptation) and QLoRA, on domain-specific datasets to improve accuracy and reduce model size.

Collaboration and Mentorship

Collaboration: Work closely with Solution Architects to understand technical blueprints and data scientists to productionize experimental models and features.

Mentorship: Provide guidance, technical direction, and training to junior developers on Generative AI technologies and software development methodologies.

Documentation: Create and maintain detailed technical documentation for all developed code, APIs, and infrastructure configurations.

Required Qualifications and Experience

Technical Expertise

Experience: 5+ years of experience in software development, with at least 2 years focused specifically on Generative AI, NLP, or applied Machine Learning.

Programming Mastery: Expert proficiency in Python and its ecosystem (Pandas, NumPy), with a strong foundation in Object-Oriented Programming (OOP) principles.

GenAI Frameworks: Hands-on experience with key GenAI libraries and frameworks, such as LangChain, LlamaIndex, Hugging Face Transformers, or PyTorch/TensorFlow.

Data & Databases: Experience working with both traditional databases (SQL, NoSQL) and specialized Vector Databases.

Cloud & Deployment: Experience with cloud computing platforms (AWS, Azure, or GCP) and familiarity with CI/CD tools, Docker, and Kubernetes for application deployment.

APIs: Proven track record of developing and consuming RESTful and GraphQL APIs.

Professional Attributes

Problem-Solving: Strong analytical and problem-solving skills with the ability to troubleshoot complex, distributed AI systems.

Communication: Excellent communication skills to articulate technical findings and development progress effectively.

Education: Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.