Background

Founded in 2010, Farsight Africa Group is a leading full-service Pan-African communications company that delivers impactful solutions to companies, governments, development organisations and individuals across East Africa. Given the incessant news cycle in both the traditional and digital media, we believe it is absolutely vital to employ reputation man...

Objective

To design, build, and deploy scalable, ethical, and high-impact AI/ML solutions that enhance Farsight Africa's products and platforms, automate and improve service delivery for our clients, and drive innovation across the organization.

Key Responsibilities

• Design, train, and deploy scalable AI/ML models for diverse business use cases.
• Integrate AI features into Farsight applications and platforms.
• Work with stakeholders to understand business needs and translate them into AI-driven solutions.
• Research and apply state-of-the-art AI methods (Generative AI, NLP, Computer Vision) to Farsight client problems.
• Contribute to innovation by exploring new tools, frameworks, and approaches to enhance AI capabilities.

Data Engineering

• Build data pipelines and preprocessing workflows for model training and inference.
• Ensure high-quality, well-governed data for model training, validation, and evaluation.

MLOps and Deployment

• Implement MLOps pipelines for continuous integration, deployment, monitoring, and improvement of models.
• Deploy models on cloud platforms using containerization and orchestration tools.
• Monitor model performance in production and retrain or optimize models as needed.

Responsible AI and Compliance

• Ensure AI solutions adhere to ethical standards, data privacy regulations, and industry best practices.
• Identify and mitigate bias, security, and privacy risks in AI systems.

Documentation and Collaboration

• Prepare technical documentation for models, data pipelines, APIs, and deployment processes.
• Collaborate with software developers, project managers, business analysts, and clients to deliver AI solutions on time.

REQUIRED SKILLS & EXPERIENCE

Academic Qualifications

• A Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Computer Engineering, or related fields.
• Professional certifications in AI/ML, Cloud (AWS, Azure, or GCP), or Data Engineering are an added advantage.

Experience & Expertise

• 3–7 years of hands-on experience in building and deploying AI/ML models in real-world applications.
• Strong programming experience in Python, R, or Java, with expertise in frameworks such as TensorFlow, PyTorch, and Scikit-learn.
• Experience with NLP, Computer Vision, Generative AI, or Recommendation Systems is highly desirable.
• Strong background in data engineering (data pipelines, APIs, SQL/NoSQL databases, and big data frameworks).
• Familiarity with cloud platforms (AWS SageMaker, Azure ML, or Google Vertex AI) and containerization (Docker, Kubernetes).
• Experience integrating AI into enterprise applications, APIs, and digital platforms.
• Understanding of MLOps best practices for continuous integration, deployment, and monitoring of ML models.

Skills & Competencies

• Knowledge of Responsible AI, Ethics, and Data Privacy in AI solutions.
• Strong problem-solving and analytical thinking skills.
• Ability to work in a fast-paced, collaborative environment.
• Excellent communication and interpersonal skills, including the ability to explain AI concepts to non-technical stakeholders.

• Design, train, and deploy scalable AI/ML models for diverse business use cases.
• Integrate AI features into Farsight applications and platforms.
• Work with stakeholders to understand business needs and translate them into AI-driven solutions.
• Research and apply state-of-the-art AI methods (Generative AI, NLP, Computer Vision) to Farsight client problems.
• Contribute to innovation by exploring new tools, frameworks, and approaches to enhance AI capabilities.
• Build data pipelines and preprocessing workflows for model training and inference.
• Ensure high-quality, well-governed data for model training, validation, and evaluation.
• Implement MLOps pipelines for continuous integration, deployment, monitoring, and improvement of models.
• Deploy models on cloud platforms using containerization and orchestration tools.
• Monitor model performance in production and retrain or optimize models as needed.
• Ensure AI solutions adhere to ethical standards, data privacy regulations, and industry best practices.
• Identify and mitigate bias, security, and privacy risks in AI systems.
• Prepare technical documentation for models, data pipelines, APIs, and deployment processes.
• Collaborate with software developers, project managers, business analysts, and clients to deliver AI solutions on time.

• Knowledge of Responsible AI, Ethics, and Data Privacy in AI solutions.
• Strong problem-solving and analytical thinking skills.
• Ability to work in a fast-paced, collaborative environment.
• Excellent communication and interpersonal skills, including the ability to explain AI concepts to non-technical stakeholders.

• A Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Computer Engineering, or related fields.
• Professional certifications in AI/ML, Cloud (AWS, Azure, or GCP), or Data Engineering are an added advantage.
• 3–7 years of hands-on experience in building and deploying AI/ML models in real-world applications.
• Strong programming experience in Python, R, or Java, with expertise in frameworks such as TensorFlow, PyTorch, and Scikit-learn.
• Experience with NLP, Computer Vision, Generative AI, or Recommendation Systems is highly desirable.
• Strong background in data engineering (data pipelines, APIs, SQL/NoSQL databases, and big data frameworks).
• Familiarity with cloud platforms (AWS SageMaker, Azure ML, or Google Vertex AI) and containerization (Docker, Kubernetes).
• Experience integrating AI into enterprise applications, APIs, and digital platforms.
• Understanding of MLOps best practices for continuous integration, deployment, and monitoring of ML models.