
Chief Generative AI Architect
8 hours ago
Job Title:
","- AI Solution Design and Development: Foster expertise in generative models, leveraging frameworks like LangChain, LlamaIndex, or similar to craft scalable applications.
- Model Optimization and Integration: Fine-tune, prompt-engineer, and optimize large language models (LLMs) for domain-specific tasks, ensuring seamless integration with APIs and databases.
- Strategic Model Deployment: Evaluate and implement model deployment strategies across various environments (on-prem, cloud, serverless, containerized), guaranteeing maximum efficiency.
- Collaborative Problem-Solving: Collaborate with cross-functional teams, including data engineers, product managers, software engineers, and other stakeholders to deliver end-to-end AI solutions.
- Staying Current with AI Advancements: Stay updated with the latest breakthroughs in AI, ML, and generative AI research, proposing innovative adoption of new technologies.
- Implementing MLOps Best Practices: Implement MLOps best practices for continuous training, deployment, and monitoring of models in production, ensuring robustness and reliability.
- Responsible AI Practices: Ensure responsible AI practices, focusing on bias mitigation, data privacy, and compliance.
Key Qualifications
","- 5+ years of experience in software development (Python preferred; familiarity with Java, C#, or Node.js a plus)
- 2+ years of hands-on experience in generative AI and LLM-based development
- Strong knowledge of AI/ML fundamentals including NLP, deep learning, embeddings, and transformers
- Experience with LLMs (e.g., OpenAI GPT, Anthropic Claude, LLaMA, Falcon, Mistral, or similar)
- Proven track record of delivering at least 2 production-grade LLM-based projects
- Experience with LangChain, LlamaIndex, Haystack, or similar frameworks
- Familiarity with vector databases (Pinecone, Weaviate, Milvus, FAISS, Chroma)
- Hands-on experience with cloud platforms (AWS, Azure, GCP) and AI services (Bedrock, VertexAI, Azure OpenAI)
- Proficiency in containerization and deployment tools (Docker, Kubernetes, CI/CD pipelines)
- Experience with MLOps tools (MLflow, Kubeflow, Weights & Biases) for model lifecycle management
- Strong problem-solving, analytical, and communication skills
What We Offer
","- A dynamic and collaborative work environment
- Opportunities for professional growth and development
- A competitive compensation package
- A comprehensive benefits program
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