EazyML
Website:
eazyml.com
Job details:
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
Click on Apply to know more.