Agentic AI Engineer

• 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

• 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

• →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