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GenAI Architect
2 months ago
We are seeking a skilled GenAI Architect to lead the design, implementation, and co-development of cutting-edge generative AI solutions at ClinDCast LLC.
About the RoleThe ideal candidate will be both a practitioner and an architect, capable of evaluating and guiding multiple AI solutions while working closely with client teams.
Key Responsibilities- Design and develop scalable, enterprise-grade AI solutions using LLMs and other ML models, with a focus on healthcare challenges.
- Ensure AI systems prioritize privacy, security, fairness, and adhere to responsible AI practices.
- Architect and implement AI outputs in various formats (JSON, arrays, HTML) for seamless integration into dashboards.
- Build extensible API integrations and low-code UI/UX solutions with short development cycles.
- Develop and optimize AI pipelines, including data preprocessing, feature extraction, model training, and evaluation.
- Work with frameworks (TensorFlow, PyTorch) and open-source platforms like Hugging Face.
- Collaborate with Product Development as a Generative AI subject matter expert.
- Create clear documentation and communicate complex AI concepts to diverse stakeholders.
- Establish best practices and standards for generative AI development within the organization.
- Bachelor's in Computer Science, AI, or related field.
- 5+ years of full-stack engineering expertise with languages like C#, Python, and proficiency in designing architecture.
- Experience in implementing enterprise AI systems in production settings, including computer vision and NLP.
- Strong understanding of natural language generation, Gen AI, transformers, LLMs, and text embeddings.
- 3+ years of experience in a technical leadership role, leading project teams and setting technical direction.
- Expertise in machine learning libraries and frameworks (PyTorch, TensorFlow, Hugging Face, LangChain, Llama Index).
- Experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies.
- Strong problem-solving skills and ability to think critically and creatively.
- Experience with self-supervised learning, transfer learning, and reinforcement learning.
- Familiarity with vector databases like Pinecone.
- Knowledge of AI ethics and responsible AI practices.
