Accountabilities:

AI Solution Design and Development: Design, develop, operationalize, and deploy AI, machine learning, and large language model (LLM)-based solutions in production environments. Build end-to-end applications using Python, APIs, cloud services, and modern AI frameworks.
Enterprise AI Architecture: Define and implement scalable AI architecture patterns covering data ingestion, model integration, model tuning, orchestration, and application layers. Develop solutions incorporating retrieval-augmented generation (RAG), vector databases, semantic layers, AI assistants, and orchestration frameworks.
Data Engineering and Integration: Design, build, and optimize data pipelines and integrations that support enterprise AI use cases. Ensure reliable, efficient, and secure data flows across business systems and operational environments.
Manufacturing Data Integration: Partner with operational technology (OT), IT, and data engineering teams to integrate manufacturing and industrial data. Develop solutions supporting predictive maintenance, quality monitoring, operational performance, and process optimization.
Enterprise Copilots and AI Enablement: Lead the implementation of enterprise copilots, including Microsoft Copilot and custom GPT-based applications. Connect AI solutions securely to internal data sources and identify opportunities to deploy AI capabilities across different business functions.
Technical Leadership and Standards: Establish reusable architecture frameworks, engineering standards, and best practices. Conduct technical design reviews, guide engineering teams, and support the successful implementation of complex AI initiatives.
Business Partnership and AI Adoption: Collaborate with business stakeholders to understand operational challenges, define requirements, assess potential value, and establish success criteria. Drive AI adoption through improvements in people, processes, systems, and data.
AI Governance and Compliance: Advise and support the internal AI governance committee, establishing guidelines for responsible AI adoption, model governance, data protection, and information security. Ensure solutions comply with enterprise policies and applicable regulatory requirements.
Security and Risk Management: Incorporate appropriate security controls, identity management, authentication, and role-based access controls into AI architectures to protect sensitive information and ensure responsible technology deployment.

Requirements

Bachelor's degree in Computer Science, Information Technology, Data Science, Engineering, or a related discipline, or equivalent practical experience.
At least 5 years of professional experience in software engineering, data engineering, solution architecture, or a closely related technical field.
Demonstrated hands-on experience designing, developing, and deploying AI/ML solutions and large language model applications.
Strong programming skills, particularly in Python, along with experience in API development, integrations, and software engineering.
Solid understanding of cloud platforms, modern data architectures, system design, and enterprise technology environments.
Proven ability to independently design and implement complex technical solutions, including scalable AI architectures and reliable data pipelines.
Experience defining architectural patterns, establishing technical standards, conducting design reviews, and providing guidance to engineering teams.
Strong knowledge of modern AI technologies and concepts, including machine learning, LLMs, retrieval-augmented generation (RAG), vector databases, semantic layers, and orchestration frameworks.
Understanding of responsible AI principles, data protection, cybersecurity, model governance, and regulatory compliance requirements.
Strong analytical and problem-solving skills, with the ability to translate complex business challenges into practical and scalable technical solutions.
Excellent communication and collaboration skills, with the ability to work effectively with technical teams, business stakeholders, and senior leadership.

How Jobgether works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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