Engineer – AI and Engineering Technology

Role Summary

GES is looking for an Engineer – AI and Engineering Technology to design build and operate production AI applications for enterprise use cases. This role combines hands-on AI/ML engineering with practical experience deploying agentic systems retrieval-augmented generation (RAG) pipelines and enterprise-integrated AI tools that run securely at scale. The ideal candidate has already shipped AI-powered products in a production environment and is comfortable owning a system end to end – from model selection and training through deployment security and cost management.

Key Responsibilities

• Design develop and deploy production AI applications for internal and external enterprise use cases including retrieval-augmented generation (RAG) pipelines and agentic AI systems

• Build and maintain autonomous agents and multi-agent workflows using frameworks such as LangChain LlamaIndex and Model Context Protocol (MCP) integrations

• Train and fine-tune AI/ML models for specific enterprise use cases and evaluate model performance for accuracy safety and cost

• Integrate AI systems with enterprise data sources and platforms (ERP SharePoint ticketing systems) to ground responses in live business data

• Own the security and governance of deployed AI systems: guardrails PII masking role-based access control sandboxed execution environments and abuse prevention

• Deploy monitor and manage containerized AI services on cloud infrastructure (AWS or Azure) including cost optimization

• Work directly with business stakeholders to translate ambiguous requirements into deployed measurable AI solutions

• Automate cross-functional workflows connecting AI systems with existing enterprise processes

Required Skills & Experience

• 4–6 years of overall experience in AI/ML or software engineering with meaningful hands-on experience building and deploying AI applications in production

• Practical experience with large language models (LLMs) – OpenAI Claude Hugging Face or equivalent

• Experience training or fine-tuning AI/ML models for specific use cases

• Hands-on experience building agentic AI systems RAG pipelines and LLM orchestration frameworks (LangChain LlamaIndex or similar)

• Working knowledge of Model Context Protocol (MCP) or similar tool-integration standards for connecting AI systems to enterprise data

• Strong programming skills in Python with experience in FastAPI and REST API development

• Experience with containerized deployment (Docker) and cloud infrastructure (AWS or Azure)

• Understanding of AI security practices: guardrails prompt safety PII masking and access control

• Demonstrated experience developing AI applications for enterprise customers or internal enterprise stakeholders

Preferred / Nice-to-Have Skills

• Experience with vector databases (Pinecone Qdrant Elasticsearch) for semantic search and retrieval

• Background in data engineering (Apache Spark Databricks Kafka) is a plus

• Familiarity with workflow automation tools (n8n or similar)

• Experience working in a consulting or client-facing engineering role across multiple industries

Education

• Bachelor's degree in Computer Science Engineering or a relat