AI Engineer
Posted Updated
Description
| AI Engineer | |
| Position Responsibilities |
2-4 DAYS ON SITE AI Engineer 2. Qualifications and ExperienceEducation- Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related field.Experience- Minimum 3 years of experience in AI/ML model development and deployment.- Experience with MLOps tools (e.g., MLflow), Docker, and cloud platforms (AWS, Azure, GCP). - Proven track record in implementing LLMs, RAG, NLP model development and GenAI solutions. Technical Skills
Soft Skills- Strong analytical and problem-solving abilities.- Excellent communication and teamwork skills. - Strategic thinking and innovation mindset. 3. Certifications (Preferred)- Microsoft Certified: Azure AI Engineer Associate- Google Machine Learning Engineer - SAFe Agile Software Engineer (ASE) - Certification in AI Ethics 4. Objectives of the Assignment- Develop and implement AI models and algorithms tailored to business needs.- Integrate AI solutions into existing systems and workflows. - Ensure ethical compliance and data privacy in all AI initiatives. - Support user adoption through training and documentation. - Support existing AI solutions by refinement, troubleshooting, and reconfiguration 5. Scope of Work and ResponsibilitiesAI Solution Development- Collaborate with cross-functional teams to identify AI opportunities.- Train, validate, and optimize machine learning models. - Translate business requirements to technical specifications. AI Solution Implementation- Develop code, deploy AI models and into production environments, and conduct ongoing model training- Monitor performance and troubleshoot issues and engage in fine-tuning the solutions to improve accuracy - Ensure compliance with ethical standards and data governance policies. User Training and Adoption- Conduct training sessions for stakeholders on AI tools.- Develop user guides and technical documentation. Data Analysis and Research- Collect, preprocess, and engineer large datasets for machine learning and AI applications.- Recommend and Implement Data Cleaning and Preparation - Analyze and use structured and unstructured data (including geospatial data) to extract features and actionable insights. - Monitor data quality, detect bias, and manage model/data drift in production environments. - Research emerging AI technologies and recommend improvements. Governance, Strategy, Support, and Maintenance- Advise WBG Staff on AI strategy and policy implications- Contribute to the team’s AI roadmap and innovation agenda. - Provide continuous support and contribute towards maintenance and future enhancements. 4. Deliverables[MP1]- Work on Proof of Concepts to study the technical feasibility of AI Use Cases[MP1]Develop, train, and deploy AI models tailored to business needs. |