AI/ML Engineer

Job description / Role

Role Overview:
The AI/ML Engineer designs, develops, and implements machine learning models and AI solutions for whole-of-government use cases. This role will focus on leveraging advanced AI technologies to address complex challenges, ensuring the successful deployment and performance of impactful AI applications across government entities.

Experience: 6-8 years

Key Skills:
• AI Model Evaluation Expertise
• R Programming
• AI Deployment Methodologies Expertise
• AI and ML
• Python SQL and NoSQL Databases
• Problem Solving Communication Skills

Qualification and Experience:
• Bachelors degree in Data Science, AI/ML Engineering, or related field. Master’s degree preferred
• Minimum 6 years of relevant experience
• Experience in AI/ML engineering, deep learning, NLP, and computer vision
• Proficiency in Python, Java, and R
• Familiarity with AI governance and compliance is a plus
• Certifications in AI/ML technologies are preferred

Key Tasks & Responsibilities:

AI Use-cases Development
• Design, build, and train AI and machine learning models using advanced algorithms, such as deep learning, reinforcement learning, and natural language processing, to support whole-of-government initiatives while ensuring alignment with strategic objectives, AI policies, and standards
• Collaborate with cross-functional teams, including data scientists, software engineers, and domain experts, to gather requirements and translate them into scalable, robust AI solutions
• Deploy AI solutions using CI/CD pipelines and containerization technologies, ensuring seamless integration with existing IT infrastructure and compliance with security, scalability, and performance standards
• Project and Performance Management
• Track and manage the progress of AI use-case implementation using project management tools, identifying potential roadblocks, bottlenecks, and proposing technical solutions to maintain project timelines and resource allocation
• Regularly update stakeholders, including senior management and external partners, on the status of AI projects, outlining key achievements, challenges encountered, and planned next steps

Post-Deployment Monitoring:
• Continuously monitor AI systems performance to gather real-time data on model behavior, system load, and user interactions, assessing the impact and value of deployed solutions
• Conduct post-implementation evaluations, including A/B testing and error analysis, to identify areas for model improvement
• Perform ongoing optimization of AI models using techniques such as retraining with updated datasets, adjusting model parameters, and fine-tuning architectures
• Implement feedback loops using automated data collection and annotation processes to refine AI models based on real-world performance data, user feedback, and changes in policy requirements

About the Company

Capgemini is a global leader in partnering with companies to transform and manage their business by harnessing the power of technology. The Group is guided everyday by its purpose of unleashing human energy through technology for an inclusive and sustainable future. It is a responsible and diverse organization of 350,000 team members in more than 50 countries. With its strong 55-year heritage and deep industry expertise, Capgemini is trusted by its clients to address the entire breadth of their business needs, from strategy and design to operations, fueled by the fast evolving and innovative world of cloud, data, AI, connectivity, software, digital engineering and platforms. The Group reported in 2022 global revenues of €22 billion.