Agentic AI Engineer

Compensation

We're looking for a Agentic AI Engineer to help architect our next-generation AI-driven products — from prototyping through production deployment. This is a customer-facing role where you'll move fluidly between solution architecture, hands-on engineering, and client conversations. Requirements Key Responsibilities: Architect and build scalable Generative AI and agentic AI applications, end to end Design LLM-powered workflows and prompt strategies for reflexive, self-learning, multi-agent systems Build intelligent AI agents using LangChain and LangGraph for use cases like NL-to-SQL, autonomous task agents, and RAG pipelines Select, customize, fine-tune, and optimize state-of-the-art LLMs Design and own full ML/GenAI pipelines — training, deployment, monitoring, lifecycle management Build APIs, microservices, and integration frameworks to bring AI into enterprise products Champion responsible AI practices — mitigating hallucinations, bias, and reliability risks Partner directly with customers, product, and engineering to turn business needs into robust AI architecture Mentor engineers and help shape our long-term AI platform strategy Required Qualifications: 6+ years in traditional ML, including 2+ years hands-on with Generative AI Strong experience with LLMs (GPT and similar), prompt engineering, and agentic systems Real-world experience with LangChain/LangGraph or similar agentic frameworks Strong Python skills — API wrappers, third-party integrations, internal tooling Solid foundation in Transformers, CNNs, RNNs — hands-on with TensorFlow, PyTorch, Scikit-learn Experience with NLP, embedding models, and vector databases Hands-on work with OpenAI, Llama/Llama2, Azure OpenAI, and other open-source models Experience designing distributed, cloud-native architectures (microservices, REST APIs) Proficiency with AWS, Azure, or GCP, plus Docker/Kubernetes MLOps/LLMOps experience — training, deployment, monitoring, lifecycle management Excellent communication skills — you can translate technical depth for non-technical stakeholders Bachelor's or Master's in CS, Data Science, Engineering, Math, Statistics, or related field Comfort with startup pace and strong ownership mentality Preferred Qualifications: LLM fine-tuning experience (LoRA, RLHF, PEFT) Performance optimization (GPU/TPU acceleration, quantization, pruning, distillation) AI observability/monitoring tool experience Familiarity with AI governance and compliance (GDPR, SOC 2) Prior consulting or solution-architecture experience shipping enterprise AI products Background in financial services, healthcare, or insurance   Must-have skills Agentic AI, Generative AI, Artificial Intelligence Good-to-have skills Machine Learning, LangChain, LangGraph