Senior Agentic AI Engineer – GenAI, LLM, RAG (Customer-Facing)

EazyML, (www.EazyML.com) recognized by Gartner, specializes in Responsible AI. Our solutions enable proactive compliance and sustainable automation for enterprises adopting AI at scale. We're also associated with breakthrough startups like Amelia.ai, giving our team exposure to cutting-edge AI products at enterprise scale.
About the Role
Are you a hands-on GenAI expert who loves building production-grade agentic systems and talking directly with customers about how AI solves real business problems? We want to hear from you.
We're looking for a Senior Agentic AI/Generative 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.
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

Work arrangement
Yes