Job Summary
We are looking for an experienced Agentic AI Engineer to design, develop, and deploy production-grade AI agent systems powered by Large Language Models (LLMs). The role involves building autonomous multi-agent architectures that can generate, evaluate, and orchestrate content and workflows with strong validation mechanisms and human-in-the-loop controls.
The ideal candidate should have hands-on experience taking LLM-powered AI solutions from prototype to production, with expertise in agent frameworks, RAG pipelines, AI evaluation, and scalable AI engineering practices.
Roles & Responsibilities
- Design and implement scalable multi-agent AI architectures, including generation, evaluation, and orchestration agents.
- Build and deploy LLM-powered generation pipelines that deliver reliable and high-quality outputs at scale.
- Develop LLM-as-a-Judge and critic-agent frameworks to evaluate AI outputs for quality, accuracy, and compliance.
- Engineer effective prompting, grounding, and Retrieval-Augmented Generation (RAG) strategies using trusted data sources.
- Implement human-in-the-loop workflows, approval mechanisms, and feedback loops to continuously improve agent performance.
- Optimize AI systems for cost, latency, scalability, and quality.
- Build observability frameworks with tracing, monitoring, and evaluation metrics for AI applications.
- Collaborate with data scientists, engineers, and business teams to deliver enterprise-grade AI solutions.
- Ensure responsible AI practices through guardrails, safety controls, and content governance mechanisms.
Primary Skills
Must-Have Skills:
- Strong proficiency in Python with hands-on experience developing production-grade AI applications.
- Experience building LLM-powered applications and generative AI solutions in production environments.
- Hands-on experience with multi-agent frameworks such as:
- LangGraph
- LangChain
- CrewAI
- AutoGen
- Strong understanding of Prompt Engineering ,Retrieval-Augmented Generation (RAG),Vector Databases / Vector Search ,AI grounding techniques , LLM evaluation methodologies
- Experience with LLM-as-a-Judge patterns, critic agents, and AI quality evaluation frameworks.
- Knowledge of major LLM providers/APIs and understanding of model selection trade-offs.
Good-to-Have Skills:
- Experience deploying LLM applications on cloud ML platforms such as:Databricks Mosaic AI, Model Serving platforms
- Exposure to MLOps / LLMOps practices, including: ,Experiment tracking, Prompt/version management, AI application observability
- Understanding of responsible AI principles, guardrails, and content governance.
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status.
