This role is for one of our clients

We're looking for a Senior 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