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Remote role increases applicant pool, but niche Agentic AI specialization reduces competition.
Deep LLM and production AI expertise required, making background transferability across industries low.
Strict 10+ years plus mandatory LLM, RAG, vector DB, MLOps, cloud, and leadership experience.
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Own architecture, technical direction, and delivery of production-grade LLM-powered agentic AI and generative AI systems, including multi-agent workflows and RAG pipelines.
Lead and mentor AI/ML and backend engineering teams, conduct technical reviews, and establish best practices for scalable, reliable AI production environments.
Collaborate with cross-functional teams to deploy AI solutions aligned with privacy, security, compliance, and business objectives.
10+ years of software engineering experience with at least 4+ years in AI/ML systems.
Minimum 2+ years hands-on experience building and deploying LLM-based or agentic AI applications in production.
Strong expertise with LLM applications, RAG, embeddings, vector search, prompt engineering, multi-agent systems, Python, API/microservices/cloud-native architecture, and cloud platforms such as AWS, Azure, or GCP.
Experience with MLOps/LLMOps tools (e.g., MLflow, LangSmith, Weights & Biases) and knowledge of LLM fine-tuning and evaluation techniques (LoRA/PEFT, RLHF).
Technical leader able to transition concepts to scalable production AI systems, combining hands-on engineering with architectural oversight.
Experienced with complex LLM/agentic AI architectures, multi-agent orchestration, and AI lifecycle management in enterprise environments.
Skilled in cross-team collaboration and communicating complex AI technical details to senior leadership and business stakeholders.