Agentic AI Engineer

Role Overview

We are looking for an experienced Agentic AI Engineer to design, build, and deploy production-grade agentic AI applications for enterprise environments.

You will work across LLM orchestration, multi-agent systems, RAG, tool calling, data engineering, evaluation, observability, and AI governance. The ideal candidate combines strong Python software engineering skills with practical experience building reliable AI systems that can operate in production at scale.

Key Responsibilities

• Design, develop, and deploy production-grade agentic AI applications for enterprise use cases.
  
• Build complex AI workflows using LangGraph and LangChain, including state machines, conditional routing, memory, tool calling, and human-in-the-loop workflows.
  
• Develop multi-agent and long-horizon AI systems using DeepAgents or comparable agent frameworks.
  
• Design and implement RAG pipelines, context management, prompting strategies, and tool-use workflows.
  
• Build reusable and well-documented agent skills, tools, and components with clear interfaces, versioning, and guardrails.
  
• Implement LLM observability and tracing using Langfuse, LangSmith, or similar platforms.
  
• Develop evaluation datasets and implement LLM evaluation, regression testing, and quality monitoring.
  
• Design and maintain modern data engineering pipelines, including ETL/ELT, batch, and streaming workflows.
  
• Work with data warehouses, lakehouses, and orchestration platforms to support AI applications.
  
• Implement data quality frameworks, including validation, lineage, freshness monitoring, and anomaly detection.
  
• Design semantic layers, metrics models, ontologies, knowledge graphs, or similar structures for AI applications.
  
• Implement security and governance practices covering access control, auditability, PII handling, and responsible AI.
  
• Collaborate with Security, Platform, Data, and Compliance teams to ensure production AI systems meet organizational standards.
  
• Translate ambiguous business requirements into scalable, maintainable, and production-ready AI solutions.
  

Requirements

Required Skills & Experience

• Proven experience building and deploying production-grade Agentic AI / LLM applications.
  
• Strong hands-on experience with LangGraph and LangChain.
  
• Experience with agent orchestration patterns including state machines, conditional routing, memory, tool calling, and human-in-the-loop workflows.
  
• Experience with DeepAgents or similar multi-agent/long-horizon AI frameworks.
  
• Practical experience with Langfuse, LangSmith, or similar LLM observability and evaluation platforms.
  
• Strong understanding of LLM application development, RAG, prompting, context management, tool use, and model selection.
  
• Advanced Python programming and software engineering skills.
  
• Experience with testing, CI/CD, Git, code reviews, and production software development practices.
  
• Experience designing and operating ETL/ELT, batch, or streaming data pipelines.
  
• Understanding of data warehouses, lakehouses, and data orchestration platforms.
  
• Experience implementing data quality, validation, lineage, freshness monitoring, and anomaly detection.
  
• Experience designing semantic layers, ontologies, metrics models, or knowledge graphs for AI applications.
  
• Experience building reusable AI tools, agent skills, guardrails, and interfaces.
  
• Understanding of AI security, governance, access control, auditability, and PII handling.
  
• Strong communication and problem-solving skills with the ability to translate business requirements into technical solutions.
  

Bonus Skills

• Experience with LLM fine-tuning, model distillation, or model serving infrastructure.
  
• Familiarity with AutoGen, CrewAI, LlamaIndex, or other agent frameworks.
  
• Experience with MLOps / LLMOps, including deployment, monitoring, and lifecycle management.
  
• Experience with offline and online AI evaluation, A/B testing, and metric-driven optimization.
  
• Experience working in finance, healthcare, life sciences, or other regulated industries.
  
• Contributions to open-source AI, agentic AI, or data engineering projects.
  
• Experience designing multi-agent architectures for complex enterprise workflows.
  

Required Skills

Agentic AI | Generative AI | LLM | LangGraph | LangChain | Python | RAG | Prompt Engineering | LLM Orchestration | AI Agents | Tool Calling | Multi-Agent Systems | Human-in-the-Loop | Context Management | Langfuse | LangSmith | LLM Evaluation | AI Observability | Data Engineering | ETL/ELT | Data Pipelines | Data Quality | Data Lineage | Semantic Layer | Knowledge Graphs | CI/CD | Git | AI Governance | PII Handling