The Senior Advisor AI Engineering will be responsible for designing, developing, and deploying state-of-the-art generative AI solutions and pipelines across GCP, Azure, and AWS. This role requires expertise in prompt engineering, model fine-tuning, embeddings, and RAG pipelines. The ideal candidate will bring technical excellence, cloud expertise, and practical experience in production-grade AI deployments and delivery for product and business stakeholders.
Key Responsibilities:
- Design, develop, and deploy generative AI solutions using models such as Google Gemini, Azure OpenAI - GPT, or AWS Nova.
- Build and maintain scalable AI pipelines including RAG pipelines, vector databases, and embedding models.
- Conduct prompt engineering and implement best practices for model performance and usability.
- Implement and optimize AI/ML workflows on cloud platforms: GCP (Vertex AI, Cloud Run, Pub/Sub, Observability), Azure, AWS.
- Perform model fine-tuning, evaluations, and experimentation to improve generative AI outputs.
- Collaborate with cross-functional teams to integrate AI solutions into applications and products.
- Ensure technical excellence, scalability, and robustness in AI systems.
- Stay up-to-date with emerging generative AI techniques and best practices.
Required Qualifications:
- Bachelor's or master's degree in computer science, Artificial Intelligence, Machine Learning, or related field.
- 10+ years of professional experience in AI/ML with at least 2+ years focused on Generative AI.
- Hands-on experience with cloud platforms: GCP - (Vertex AI, Cloud Run, Pub/Sub), Azure (OpenAI Service /Azure Foundry), or AWS Bedrock.
- Expertise in prompt engineering, embeddings, RAG pipelines, and vector databases.
- Strong Python programming skills and experience with ML frameworks (Hugging Face, PyTorch, TensorFlow).
- Experience implementing AI pipelines, MLOps, and model lifecycle management in production environments.
- Experience in model fine-tuning, optimization, evaluation, and deployment.
- Strong understanding of machine learning concepts, data pipelines, and production-ready AI systems.
- Proven ability to consult with business stakeholders to deliver effective, technically excellent, scalable AI solutions.
Preferred Skills:
- Familiarity with vector databases (Pinecone, Chroma) and RAG frameworks.
- Familiarity with MLOps practices, CI/CD pipelines, and containerization (Docker/Kubernetes).
- Strong presentation, communication, and stakeholder advisory skills.
- Background in AI consulting, innovation labs, or R&D environments is a plus.
- Experience in the healthcare domain.
