Jobs at Noemdek Limited

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:

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

Requirements:

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

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We are seeking a Data Systems & AI Insights Engineer to own our data infrastructure and analytics pipelines that power AI models. This hybrid role combines data engineering (ensuring clean, structured, and accessible datasets) with data science (model evaluation, feature engineering, performance analytics). You will work closely with the AI engineering team to provide the high-quality data needed for effective AI solutions.

Key Responsibilities:

• Design, build, and maintain data pipelines for AI/ML training and inference.
• Clean, normalize, and structure large datasets from multiple sources.
• Create datasets for supervised/unsupervised learning tasks.
• Perform exploratory data analysis (EDA) to identify patterns and insights.
• Collaborate with AI engineers to create features that improve model performance.
• Build dashboards and reporting tools to monitor AI system outputs.
• Ensure data quality, security, and compliance with regulations.

Requirements:

• Strong SQL and Python skills for data manipulation and automation.
• Experience with data engineering tools (Airflow, dbt, Kafka, Spark, etc.).
• Hands-on experience with cloud data platforms (AWS Redshift, BigQuery, Snowflake, etc.).
• Familiarity with data science workflows (pandas, NumPy, scikit-learn, visualization libraries).
• Knowledge of data quality and validation best practices.
• Experience building ETL/ELT pipelines.