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Remote role and mid-level seniority increase applicant pool despite niche LLM specialization.
Requires deep LLM, LangChain, MLOps and cloud expertise, limiting cross-industry transferability.
Multiple mandatory years plus specialized LLM, LangChain, MLOps, cloud and deployment skills make selection highly selective.
Architect and build end-to-end scalable Generative AI and agentic AI applications, including multi-agent LLM-powered workflows.
Develop and own full ML/GenAI pipelines, including training, deployment, monitoring, and lifecycle management.
Partner with customers and internal teams to translate business requirements into robust AI architectures and mentor engineers, influencing long-term AI platform strategy.
6+ years in traditional ML with at least 2 years hands-on experience in Generative AI.
Proficiency with LLMs (GPT and similar), prompt engineering, LangChain/LangGraph or similar agentic AI frameworks.
Strong Python programming skills and experience with TensorFlow, PyTorch, Scikit-learn.
Experience with cloud platforms (AWS, Azure, or GCP), Docker/Kubernetes, and MLOps/LLMOps workflows.
Experienced in architecting and deploying enterprise-scale AI products in a customer-facing role bridging technical and non-technical stakeholders.
Deep expertise in agentic AI systems, prompt design, vector databases, and distributed cloud-native architectures.
Comfortable working in a startup-like environment with strong ownership and the ability to mentor and lead AI engineering efforts.