Role Summary
The AI/ML Engineer is tasked with creating, implementing, and refining machine learning and generative artificial intelligence solutions. Operating at the convergence of data engineering, analytics, product strategy, and machine learning, this position focuses on constructing systems capable of aggregating, organizing, and analyzing vast quantities of African data. The primary objective is to convert this raw information into searchable intelligence, automated insights, predictive analytics, and decision-support mechanisms.
Key Responsibilities
- Architect, engineer, and launch machine learning models tailored for business intelligence, market forecasting, classification, recommendation engines, and other strategic applications.
- Industrialize AI models that transform diverse African data sources into actionable business insights.
- Construct predictive and analytical frameworks covering sectors such as economics, financial markets, industries, corporate entities, and consumer trends.
- Assess various machine learning methodologies to select optimal models based on metrics including accuracy, scalability, cost-efficiency, and business impact.
- Track model performance metrics and iterate on algorithms to enhance accuracy as new data streams become available.
- Engineer applications leveraging Large Language Models (LLMs) and foundational models to drive innovation.
- Architect Retrieval-Augmented Generation (RAG) systems that allow AI tools to access and reason over internal datasets, research archives, and knowledge bases.
- Create AI-driven research assistants designed to search, synthesize, and interpret extensive information repositories.
- Design intelligent query interfaces specifically for African economic, industrial, and business data.
- Execute prompt engineering, structured output generation, function calling, tool integration, and agentic workflow implementation.
- Optimize LLM performance regarding latency, cost, and accuracy.
- Investigate model fine-tuning and adaptation strategies where they offer distinct advantages over standard prompting or RAG techniques.
- Collaborate on the creation of the company's proprietary African Intelligence Engine.
- Develop AI functionalities capable of interpreting African markets, economic conditions, industries, and business landscapes.
- Create structured knowledge graphs representing African nations, corporations, sectors, markets, and economic indicators.
- Integrate fragmented datasets to generate comprehensive intelligence.
- Build systems capable of detecting relationships, trends, anomalies, and emerging patterns within African data.
- Facilitate the progression of data through the value chain: from raw data to information, insight, intelligence, and finally, decision-making.
- Contribute to the evolution of proprietary AI technologies.
- Partner with data engineering teams to establish robust data pipelines for AI and ML initiatives.
- Clean, transform, and prepare both structured and unstructured data for model training and development.
- Engineer pipelines supporting model training, inference, and evaluation cycles.
- Manage interactions with APIs, databases, cloud storage, and external data providers.
- Establish protocols for data validation, quality assurance, and feature engineering.
- Define organizational standards for data readiness for AI applications.
- Engineer AI agents capable of executing complex, multi-step research and analytical workflows.
- Construct automated workflows where AI independently retrieves data, analyzes information, generates outputs, and initiates downstream actions.
- Integrate AI models with internal databases, APIs, analytical tools, and legacy software systems.
- Implement human-in-the-loop verification processes where expert judgment is necessary.
- Identify repetitive research and analytical tasks suitable for AI automation.
- Deploy AI and machine learning models into live production environments.
- Implement processes for model monitoring, version control, testing, and evaluation.
- Track model reliability, latency, and inference costs to ensure operational efficiency.
- Guarantee that AI applications maintain scalability and reliability as user demand grows.
- Collaborate with product, research, analytics, and business units to translate business challenges into AI-driven solutions.
- Rapidly prototype AI-enhanced products and features.
- Scale successful prototypes into robust production applications.
- Engage in product discovery, experimentation, and user testing phases.
- Identify avenues for creating new revenue streams through AI-powered products and services.
- Assist in the development of APIs and AI service offerings.
- Stay current with advancements in AI, machine learning, generative AI, LLMs, and AI agents.
- Evaluate emerging models, frameworks, and tools to assess their strategic relevance.
- Conduct experiments and proof-of-concept projects to validate new technologies.
- Enrich the company's technical knowledge base and AI research library.
- Prioritize the development of proprietary capabilities over exclusive reliance on third-party AI platforms.
Required Qualifications
- Bachelor's degree in Computer Science, Computer Engineering, Software Engineering, Mathematics, Statistics, Data Science, Electrical/Electronic Engineering, or a related quantitative field.
- 1-2 years of professional experience in AI/ML, machine learning engineering, data science, or a comparable technology domain.
- Proficient programming abilities in Python.
- Demonstrated hands-on experience in developing and deploying machine learning models.
- Familiarity with contemporary AI/ML frameworks and libraries.
- Experience working with APIs, databases, and data pipeline architectures.
- Proficiency with at least one major cloud computing platform, such as AWS, Azure, or Google Cloud.
- Solid grasp of software engineering principles and Git-based version control workflows.