Developer - AI Engineer

Looking for an AI Engineer for a 7 month contract with possible long term extension.

AI Engineer: The AI Engineer will play a pivotal role in designing, developing, and deploying artificial intelligence solutions that enhance operational efficiency, automate decision-making, and support strategic initiatives for the environmental and social specialists within the client. This role is central to the VPU s digital transformation efforts and will contribute to the development of scalable, ethical, and innovative AI systems.

Qualifications and Experience

Education: 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, Google Cloud Platform).
• Proven track record in implementing LLMs, RAG, NLP model development and GenAI solutions.

Technical Skills:

• Skilled in Azure AI/Google Vertex Search, Vector Databases, fine-tuning the RAG, NLP model development, API Management (facilitates access to different sources of data)
• Proficiency in Python, TensorFlow, PyTorch, and NLP frameworks.
• Expertise deep learning, computer vision, and large language models.
• Familiarity with REST APIs, NoSQL, and RDBMS.

Certifications (Preferred):

• Microsoft Certified: Azure AI Engineer Associate
• Google Machine Learning Engineer
• SAFe Agile Software Engineer (ASE)
• Certification in AI Ethics

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

Scope of Work and Responsibilities:

AI 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
• Analyse 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 client's 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.

Deliverables:

• Work on Proof of Concepts to study the technical feasibility of AI Use Cases
• Functional AI applications integrated into business systems.
• Documentation of model/application architecture, training data, and performance metrics.
• Training materials and user guides.
• Develop, train, and deploy AI models tailored to business needs