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Strong employer and metro location but specialized GenAI requirements moderate competition.
Requires specialized LLM, RAG, and MLOps expertise, limiting cross-industry transferability.
Extensive mandatory GenAI, LLMOps, and production leadership experience increases screening rigor.
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Lead design and development of enterprise-scale multi-agent and multimodal generative AI systems including LLMs, retrieval-augmented generation (RAG), and agentic orchestration.
Own production-grade deployment of LLMOps and MLOps pipelines on cloud platforms (AWS Databricks), ensuring reliability, scalability, and AI governance compliance.
Drive innovation in Gen AI by implementing advanced techniques such as RLHF, PEFT, LoRA, and spearhead responsible AI practices including bias detection and explainability.
Bachelor's or Master's degree in Computer Science, AI/ML, or Engineering.
Significant hands-on experience leading and delivering complex generative AI or ML engineering programs in production environments.
Expertise with LLM ecosystems (OpenAI, Anthropic, Gemini, Hugging Face, LangChain) and multimodal AI architectures (BERT, CLIP, LLaVA, GPT-4o, Gemini).
Strong Python skills including async programming and API development; proficiency with cloud AI infrastructure (AWS SageMaker, Bedrock, Azure OpenAI, or GCP Vertex AI); SQL proficiency also required.
Senior technical leader capable of setting engineering standards and leading cross-functional AI teams in enterprise environments focused on generative AI.
Deep expertise in architecting and operationalizing advanced generative AI, agentic systems, and hybrid AI/ML decision systems.
Experience balancing cutting-edge AI innovation with production-grade reliability, responsible AI governance, and stakeholder communications at scale.