AI ML Engineer @ SA Technologies

• Design, develop, and deploy Generative AI solutions using state-of-the-art models like GPT-4, LlaMa, Claude, Gemini, or Pi.
  
• Fine-tune large language models (LLMs) for specific use cases and optimize them for performance and accuracy.
  
• Implement and integrate frameworks such as LangChain and LlaMaIndex for building scalable AI/ML applications.
  
• Develop and implement Retrieval-Augmented Generation (RAG) pipelines for efficient knowledge retrieval and contextual response generation.
  
• Create and refine prompts using advanced prompt-engineering techniques to enhance model performance for various NLP tasks.
  
• Collaborate with cross-functional teams to define technical requirements and develop innovative AI/ML solutions tailored to business needs.
  
• Conduct research and experimentation to evaluate and integrate new LLMs, APIs, and generative AI tools.
  
• Implement best practices for MLOps, ensuring scalability, versioning, and efficient deployment of AI/ML models.
  
• Monitor, analyze, and improve the performance of AI/ML systems in production environments.

Required Skills:

• Strong programming skills in Python with hands-on experience in AI/ML libraries and frameworks.
  
• Proven expertise in working with large language models (LLMs) like GPT-4, LlaMa, Claude, Gemini, or Pi.
  
• Proficiency in LangChain, LlaMaIndex, and Retrieval-Augmented Generation (RAG).
  
• Experience in fine-tuning pre-trained language models for specific tasks or domains.
  
• Advanced knowledge of prompt engineering and designing prompts for optimal LLM performance.
  
• Familiarity with OpenAI, Anthropic, or similar APIs and platforms.
  
• Strong understanding of NLP concepts, embeddings, vector databases, and transformers.

Preferred Skills:

• Experience with Generative AI tools and frameworks like Hugging Face, OpenAI API, or Cohere.
  
• Familiarity with vector databases (e.g., Pinecone, Weaviate, Milvus) for RAG implementations.
  
• Knowledge of MLOps practices, including model versioning, CI/CD pipelines, and containerization (Docker, Kubernetes).
  
• Exposure to cutting-edge generative AI models and platforms like Claude, Gemini, or Pi.
  
• Understanding of cloud platforms (AWS, GCP, Azure) for deploying AI/ML models at scale.

Education & Certifications:

• Bachelor or Master degree in Computer Science, Artificial Intelligence, Machine Learning, or related fields.
  
• Certifications in AI/ML or Generative AI platforms (e.g., OpenAI, Hugging Face, Google Cloud AI) are a plus.