AI Solution Engineer (LLM & Agentic AI)

Evonik Industries AG; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Evonik Industries AG in Singapore seeks an experienced AI Engineer to design, implement, and scale LLM-based solutions across enterprise contexts. You will own end-to-end delivery from prototypes to production, integrate data pipelines and APIs with enterprise systems, and advocate responsible AI practices.

You will collaborate with business stakeholders, translate needs into technical options, and contribute reusable components for future projects.

Qualifications

• Hands-on experience in software engineering and AI application engineering.
• Strong experience with LLM-based solution development, including RAG and context engineering.
• Solid data integration skills across structured and unstructured data, APIs, and data quality.
• Proficient Python engineering for APIs or backend components and SQL for AI delivery.
• Experience using Azure cloud environments for managed LLM services and deployment.
• Familiarity with modern delivery workflows: Git, Azure DevOps, CI/CD, containers, testing, monitoring.

Responsibilities

• Solution Delivery: understand business problems, shape AI options, prototype, and deliver to production.
• AI Engineering: design, build, evaluate, and improve LLM-based applications and agent workflows.
• End-to-End Implementation: own implementation from prototype to production, including APIs, data pipelines and monitoring.
• Data Engineering and Integration: build data pipelines for AI use cases with reliable ingestion and access control.
• Enterprise System Integration: connect AI solutions with enterprise systems and APIs.
• Quality and Responsible Operation: ensure testing, security, privacy, observability, and cost awareness.
• Reusable Delivery Assets: contribute components, prompts, evaluation patterns, and templates.

Skills

LLM-based solution development

Python

APIs/Backend

Azure cloud

Data integration

CI/CD

Git/Azure DevOps

Retrieval-augmented generation

Prompt engineering

Agent workflows

Tools

Azure DevOps

Git

Docker

Job description

What we offer

At Evonik, you're part of a global chemical company working in many sectors that go beyond chemistry. From lipids for tomorrow's medicines, biosurfactants for green detergents, additives for plastics recycling or membranes to help drive forward the energy transition, you'll play an essential part in helping the world's most essential industries succeed.

Our success hinges on a diversity of ideas powered by people, not egos. Whether you're a scientist or engineer, new to the team or established, in business, production, or anything in between - we cheer each other on. Because we believe we can do anything. Explore everything. And Be Part of Something Special!

Find out more about the many benefits we offer:

https://www.evonik.com/en/careers/why.html

Meet the team and get to know the people behind Evonik:

https://www.evonik.com/en/news/meet-the-team.html

RESPONSIBILITIES

• Solution Delivery: Work with internal customers to understand business problems, shape feasible AI solution options, validate value through prototypes, and deliver solutions into productive use.
• AI Engineering: Design, build, evaluate, and improve LLM-based applications such as RAG solutions, knowledge assistants, AI agents, tool-using workflows, and automation flows.
• End-to-End Implementation: Own the technical implementation from prototype to production, including application logic, APIs, data pipelines, orchestration, testing, deployment, documentation, monitoring, and continuous improvement.
• Data Engineering and Integration: Build the data and knowledge pipelines required for AI use cases, with a focus on reliable ingestion, preparation, retrieval, and access-control-aware use of information.
• Enterprise System Integration: Connect AI solutions with relevant enterprise systems (e.g. Microsoft, SAP), APIs, knowledge repositories, and operational data sources.
• Quality and Responsible Operation: Apply testing, evaluation, responsible AI practices, security, data protection, observability, cost awareness, and production support to ensure trustworthy enterprise use.
• Reusable Delivery Assets: Contribute reusable components, prompts, evaluation approaches, RAG and agent patterns, pipeline templates, and implementation guidance based on delivered solutions.

REQUIREMENTS

• Several years of hands-on experience in software engineering, AI application engineering, solution delivery, data engineering and/or enterprise integration with a proven ability to bring solutions into productive use.
• Strong practical experience with LLM-based solution development, especially retrieval-augmented generation, prompt and context engineering, tool use, and agentic workflows.
• Solid data integration skills for AI use cases, including working with structured and unstructured data, APIs, data quality, and operational reliability.
• Strong software engineering skills, especially in Python, with experience building APIs or backend components and applying SQL where needed for AI solution delivery.
• Practical experience using Azure or comparable cloud environments to deliver AI solutions, including managed LLM services, retrieval technologies, secure integration, and deployment of use-case-specific components.
• Familiarity with modern delivery workflows, including version control such as Git, Azure DevOps or comparable tools, CI/CD basics, containerized deployments, testing, monitoring, and support for delivered AI solutions.
• Ability to work directly with business users, clarify ambiguous needs, translate problems into technical options, validate value through prototypes, and communicate trade-offs clearly.
• Good understanding of how to evaluate and operate AI solutions responsibly, including quality, security, data protection, cost awareness, and safe use of agentic workflows.
• Additional experience with enterprise platforms, industrial systems, or scientific and technical data environments would be valuable.