Akoni Technologies

Role Summary As part of our innovation-led strategy, we're looking for a talented AI/ML Engineer to join our growing team. We are actively developing conversational AI platforms, intelligent chatbots, and autonomous AI agents capable of interacting with users, automating business processes, and learning from real-time data. Our solutions are being built using state-of-the-art platforms such as AWS Bedrock, enabling us to quickly deploy scalable, secure, and customizable foundation models. As an AI/ML Engineer at Akoni Technologies, you’ll design, develop, and deploy AI/ML solutions that drive business outcomes and product innovation. You'll work at the intersection of data science, engineering, and product development to bring intelligence to our digital platforms. This role is ideal for someone passionate about deep learning, NLP, AI agents, and scalable architectures using modern cloud environments. Key Responsibilities Design and implement end-to-end AI/ML models to solve diverse business problems. Collaborate across teams to identify AI/ML use cases and deliver data-driven innovations. Build and maintain data pipelines and feature engineering workflows for large-scale datasets. Develop and optimise deep learning models for NLP, chatbots, and autonomous AI agents. Leverage AWS Bedrock to deploy, fine-tune, and integrate foundation models into real-time applications. Deploy models in cloud environments (AWS, Azure, GCP) using containerised solutions (Docker/Kubernetes). Integrate ML models into production systems, ensuring scalability, resilience, and performance. Perform A/B testing, model evaluation, and continuous tuning for accuracy and efficiency. Stay ahead of emerging trends in AI agent frameworks, generative AI, and multi-modal models. Create technical documentation, onboarding material, and provide training to cross-functional teams. Required Qualifications & Experience Required Qualifications & Experience Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, or related fields. 3–5 years of hands-on experience developing and deploying ML models in production. Strong programming skills in Python and experience with ML libraries such as: TensorFlow, PyTorch, Scikit-learn, Transformers, etc. Proven experience in developing chatbots, LLM-based agents, or conversational AI systems. Deep understanding of NLP, deep learning architectures, computer vision, and reinforcement learning. Experience with cloud-native AI deployments, especially using AWS Bedrock, SageMaker, or similar. Proficiency in data engineering and handling high-volume, unstructured data. Experience with MLOps tools, version control (e.g., MLflow, Git), and CI/CD practices. Strong communication and problem-solving skills — able to translate business needs into technical AI solutions. AI/ML certifications (e.g., AWS Certified ML, TensorFlow Developer) are a strong plus.