AI Engineer (ML + MLOps) Job at NoemDek Limited

NoemDek Limited is an EPC Company incorporated in Nigeria in 2012, to provide specialized services to the Oil & Gas, Power Generation, FMCGs, etc. Over the past five years, the firm and its international partners have supported clients on large infrastructure projects and after-sales services to ensure the assets are maintained effectively and a client’s operations run smoothly.

We are recruiting to fill the position below:

Job Position: AI Engineer (ML + MLOps)

Job Location: Ikoyi, Lagos

Role Overview:

• As our AI Engineer, you will own the design, development, and deployment of AI models within our applications.
• You will work closely with the team to transform business requirements into production-ready AI solutions, leveraging both custom models and third-party APIs.
• You’ll also be responsible for setting up and managing robust MLOps pipelines to ensure continuous improvement and scalability of AI systems.

Key Responsibilities

• Build, fine-tune, and deploy AI/ML models (NLP, CV, LLMs, or domain-specific).
• Integrate AI into existing and new application backends.
• Develop and maintain MLOps pipelines for training, deployment, and monitoring.
• Optimize AI features for latency, scalability, and cost-efficiency.
• Collaborate with data specialists and product team to ensure models are trained on high-quality datasets.
• Track model performance and iterate based on feedback and metrics.

Requirements

• Proven experience as an ML Engineer or AI Engineer, with production-level deployments.
• Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, Hugging Face).
• Experience with LLMs (fine-tuning, prompt engineering, embeddings).
• Familiarity with MLOps tools (MLflow, Kubeflow, AWS Sagemaker, Azure ML, GCP Vertex AI).
• Experience integrating AI into APIs or full-stack applications.
• Strong understanding of cloud environments and containerization (Docker, Kubernetes).
• Experience with vector databases (Pinecone, Weaviate, Milvus).
• Experience with reinforcement learning or multi-modal AI models.