The role of the ML/AI Engineer involves designing, building, and deploying machine learning systems that provide tangible value to clients. This is a hands-on position requiring a strong technical foundation coupled with the ability to understand and solve complex business problems. The successful candidate will work closely with client teams, ensuring requirements are translated into robust, production-grade solutions.
Techno Brain, the hiring company, is the first indigenous African company to achieve CMMI Level-5 assessment, maintaining the highest quality standards for software development. The company operates in over 21 countries across Africa, USA, UK, India, and UAE, holding leadership positions in domains such as Public Financial Management, Digital Identity Management, Tax & Customs, IT Training, Business Process Outsourcing, and various e-Government solutions and digital products.
Key Responsibilities (What You Will Do)
- Design and implement machine learning pipelines for classification, Natural Language Processing (NLP), and computer vision use cases.
- Build conversational AI systems utilizing frameworks like LangChain, LangGraph, and large language model (LLM) APIs.
- Develop Retrieval-Augmented Generation (RAG) pipelines for efficient document processing and knowledge retrieval.
- Deploy models to production environments, ensuring they include proper monitoring and observability capabilities.
- Collaborate effectively with cross-functional teams to clearly define requirements and measurable success metrics.
- Conduct rigorous experiments, A/B tests, and comprehensive model evaluations to validate hypotheses.
- Write clean, tested, and well-documented code.
- Mentor junior engineers and actively contribute to the establishment of technical best practices within the team.
Qualifications and Experience (What You Bring)
- 3+ years of experience in machine learning or data science roles.
- BSc/MSc in Computer Science, Data Science, Mathematics, or a related quantitative field.
- Strong Python skills combined with practical experience in machine learning libraries such as scikit-learn, PyTorch, or TensorFlow.
- Mandatory hands-on experience with LangChain and LangGraph.
- Proven experience deploying ML models to production environments.
- Solid theoretical and practical understanding of NLP concepts, including embeddings, transformers, and retrieval methods.
- Familiarity with MLOps practices, including versioning, monitoring, and Continuous Integration/Continuous Deployment (CI/CD) specifically for ML systems.
- Experience utilizing vector databases such as Pinecone, Weaviate, or ChromaDB.
Nice to Have:
- Documented contributions to open source ML projects.
