Senior Agentic AI Engineer (ID: 4120)

Senior Agentic AI Engineer (ID: 4120)

As a Senior Agentic AI Engineer, you will

• Design, develop, and deploy enterprise-grade AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and multi-agent systems.

• Build intelligent AI applications, automation workflows, virtual assistants, and data-driven decision-support solutions.

• Develop and optimize AI workflows using frameworks such as LangChain, LlamaIndex, Azure OpenAI, or similar technologies.

• Implement embedding strategies and integrate vector databases to enable efficient semantic search and knowledge retrieval.

• Design, build, and maintain scalable AI and data pipelines on cloud platforms such as Microsoft Azure and AWS.

• Implement MLOps practices for model deployment, monitoring, evaluation, performance optimization, and retraining.

• Collaborate with business stakeholders, data engineers, software engineers, and other technical teams to deliver AI-driven solutions.

What You Bring to the Table

• 8–10 years of overall experience in Software Engineering, Data Engineering, AI Engineering, or related technical fields.

• Strong programming skills in Python and SQL.

• Hands-on experience developing LLM-based applications and Agentic AI solutions.

• Practical experience with RAG architectures, embeddings, vector databases, and multi-agent systems.

• Experience with LangChain, LlamaIndex, Azure OpenAI, or equivalent AI frameworks and services.

• Knowledge of Databricks, Apache Spark, and Apache Airflow for data processing and orchestration.

• Experience working with cloud platforms, particularly Microsoft Azure and/or AWS.

• Familiarity with MLOps practices, model lifecycle management, and production AI deployments.

• Experience with Docker, Kubernetes, and CI/CD pipelines.

• Understanding of AI security, governance, monitoring, and enterprise-level deployment requirements.

You should possess the ability to

• Design and implement scalable, reliable, and maintainable Agentic AI solutions.

• Develop and optimize LLM workflows to improve response quality, accuracy, and performance.

• Integrate enterprise data sources with AI applications using RAG, embeddings, and vector search.

• Build and manage AI and data pipelines across cloud environments.

• Evaluate AI models and workflows, identify performance issues, and implement improvements.

• Deploy, monitor, troubleshoot, and maintain AI applications in production environments.

• Apply containerization, orchestration, and CI/CD practices to streamline AI solution delivery.

• Work effectively with cross-functional teams and translate business requirements into technical solutions.

What We Bring to the Table

• The opportunity to work on enterprise-scale Generative AI and Agentic AI solutions.

• Exposure to modern AI technologies, LLMs, RAG architectures, and multi-agent systems.

• The opportunity to develop scalable, cloud-based AI applications and intelligent automation solutions.

• A collaborative, Agile working environment focused on innovation and production-ready AI engineering