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Senior AI Engineer – LLM, Machine Learning
inventYOU IT Consulting. Develop, integrate, and maintain AI and Machine Learning models within business applications .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing and deploying AI and Machine Learning models, with a strong focus on LLM-based solutions and data pipeline optimization. Proficient in MLOps practices and ensuring compliance with data governance and ethical AI standards.
Highest-signal resume keywords
AI Solution DevelopmentLLM-Based SolutionsMLOps PracticesData Pipeline DesignCloud-Based AI Integration
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
PythonSQLJavaMachine Learning FundamentalsData Pipeline MaintenanceAPIsMicroservicesDockerKubernetesModel Evaluation
Tools & Technologies
AzureAWSGCP
Certifications & Qualifications
Microsoft Certified: Azure AI EngineerAWS Certified Machine LearningGoogle Professional Machine Learning Engineer
Industry Keywords
AIMachine LearningGenerative AIData GovernanceEthical AI
Tech Stack
Tools & technologiesAWSAzureCloudDockerGoogle Cloud PlatformJavaKubernetesMicroservicesPythonSQL
About the role
Key responsibilities & impact- Develop, integrate, and maintain AI and Machine Learning models within business applications
- Design and optimize data pipelines for model training, evaluation, and deployment
- Build LLM-based and Generative AI solutions, including RAG, prompt engineering, and fine-tuning
- Translate business requirements into practical AI solutions with Product Owners and domain experts
- Deploy and operate models using MLOps practices, including CI/CD, monitoring, and versioning
- Evaluate model performance, fairness, reliability, and overall quality
- Integrate cloud-based AI services across Azure, AWS, and/or GCP
- Develop and integrate APIs and microservices supporting AI capabilities
- Ensure compliance with data governance, security, and ethical AI requirements
- Document AI workflows, architectures, and operational procedures
- Support cross-functional teams in adopting and integrating AI capabilities
Requirements
What you’ll need- Minimum 4 years of experience designing and implementing AI solutions, including LLM-based solutions
- University degree with at least 6 years of IT experience, or non-university degree with at least 12 years of IT experience
- Strong programming experience with Python, SQL, and/or Java
- Strong understanding of Machine Learning fundamentals and the model lifecycle
- Experience designing and maintaining data pipelines
- Experience with LLMs and Generative AI, including practical implementation of LLM-based solutions
- Knowledge of APIs and microservices
- Experience with Docker and Kubernetes
- Understanding of production AI/ML deployment, monitoring, and lifecycle management
- Strong understanding of data governance, security, and responsible AI principles
- At least one mandatory professional certification: Microsoft Certified: Azure AI Engineer, AWS Certified Machine Learning, Google Professional Machine Learning Engineer, or an equivalent recognized certification
Benefits
Comp & perks- Work on advanced AI, Machine Learning, and Generative AI initiatives
- Build production-ready LLM and enterprise AI solutions
- Work with modern cloud, MLOps, and AI technologies
- Collaborate with experienced technology and domain professionals
- Continue developing your expertise in one of the fastest-growing areas of technology