Full-Time AI Engineer

We are seeking a highly skilled and forward-thinking AI Engineer to join our AI research team for ERP. This role plays a pivotal part in shaping the future of enterprise software by designing, developing, and deploying AI systems, intelligent agents, and orchestration frameworks, while also focusing on streamlining the deployment, monitoring, and continuous improvement of AI solutions (e.g., MLOps, LLMOps).

Your primary responsibility will be AI research that supports intelligent automation and orchestration within enterprise systems.

Key Responsibilities:

• Generative AI Foundations: Design and implement GenAI solutions using LLMs and RAG capabilities.
• Agentic Architecture & Orchestration: Build modular, task-specific agents using frameworks like Semantic Kernel or similar.
• Infrastructure & Deployment: Design cloud-native, scalable infrastructure for agent workloads (Azure, AWS, GCP).
• Data Integration & Event-Driven Systems: Collaborate with data engineers and architects to integrate structured and unstructured data sources.
• Collaboration & Leadership: Work closely with architects, data scientists, data engineers, and product leads to align AI foundational architecture with business goals.

Requirements:

• Technical Skills: Proficiency in leveraging Language Models (e.g., LLMs, SLMs), Retrieval-Augmented Generation (RAG), and prompt engineering.
• Infrastructure & Deployment: Experience with design and implementation of MLOps pipelines at scale.
• Agentic Architecture & Orchestration: Experience in agent development frameworks such as Semantic Kernel.
• Data Integration & Event-Driven Systems: Familiarity with event-driven systems like Kafka, Flink, and Spark Streaming.
• Programming: Strong programming skills in Python, .NET, and SQL/NoSQL databases.

Preferred Experience

• 3+ years in software engineering with a strong foundation in modern engineering practices.
• Experience in enterprise software, ERP systems, and process automation.
• Hands-on experience with GenAI concepts such as Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), or agent orchestration (A2A).
• Experience in AI/ML lifecycle management (AI operationalization) and governance.

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