AI/ML Developer

Responsibilities

• 1 Model Design & Training
• Design, train, and fine-tune Small Language Models (SLMs) using frameworks such as PyTorch, TensorFlow, or JAX.
• Conduct experiments with supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and instruction tuning.
• Implement efficient training pipelines leveraging distributed training (DDP, FSDP) across GPU/TPU clusters.
• Perform hyperparameter optimisation, ablation studies, and model selection based on benchmark results.
• Develop and maintain data pipelines for collecting, cleaning, tokenising, and pre-processing large-scale training corpora.
• 2 Model Evaluation & Quality
• Define and implement evaluation frameworks including perplexity, BLEU, ROUGE, BERTScore, and task-specific benchmarks.
• Conduct red-teaming, bias analysis, and safety evaluations to ensure responsible AI deployment.
• Benchmark models against established baselines (e.g., GPT-2, Phi, Mistral) and track performance over iterations.
• Collaborate with QA teams to build regression suites for model versioning and continuous evaluation.
• 3 MLOps & Deployment
• Containerise and deploy models using Docker, Kubernetes, and cloud-native ML platforms (AWS SageMaker / GCP Vertex AI / Azure ML).
• Build and maintain model registries, experiment tracking (MLflow, Comet), and reproducible training pipelines.
• Optimise inference performance through quantisation (INT8, INT4), pruning, distillation, and ONNX/TensorRT conversion.
• Monitor model drift, data drift, and performance degradation in production; implement automated retraining triggers.
• 4 Research & Innovation
• Stay current with state-of-the-art NLP/LLM research; prototype and validate new techniques from published literature.
• Contribute to internal knowledge sharing, technical documentation, and model cards.
• Explore parameter-efficient fine-tuning (PEFT) methods such as LoRA, QLoRA, and Adapter layers.
• Investigate and apply mixture-of-experts (MoE), retrieval-augmented generation (RAG), and agentic workflows as needed.
• 5 Collaboration & Stakeholder Management
• Work with product managers and domain experts to translate business requirements into model objectives and KPIs.
• Communicate model capabilities, limitations, and trade-offs clearly to both technical and non-technical stakeholders.
• Participate in architecture reviews, sprint planning, and cross-functional design discussions.
• Required Qualifications
• 1 Education
• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Statistics, or a related quantitative field.
• Equivalent professional experience with a strong portfolio of delivered AI/ML projects will be considered.
• 2 Experience
• 4 - 6 years of professional experience in machine learning or NLP engineering.
• Minimum 2 years of direct, hands-on experience in training or fine-tuning language models (LLMs or SLMs).
• Demonstrable experience taking a model from dataset preparation through to production deployment.
• 3 Technical Skills - Core
• Programming: Python (proficient); familiarity with C++/CUDA a plus.
• Deep Learning Frameworks: PyTorch (primary), TensorFlow or JAX.
• NLP Libraries: Hugging Face Transformers, Datasets, PEFT, TRL, Accelerate.
• Training Infrastructure: Multi-GPU training, FSDP, DeepSpeed ZeRO stages 1-3.
• Data Engineering: Pandas, NumPy, Apache Spark or Dask for large-scale data prep.
• Vector DBs & Retrieval: FAISS, Pinecone, Weaviate, or equivalent.
• Cloud Platforms: AWS, GCP, or Azure - ML-specific services (SageMaker, Vertex AI, etc.).
• Version Control & CI/CD: Git, GitHub/GitLab, MLflow or W&B for experiment tracking.
• 4 Technical Skills - Nice to Have
• Experience with multimodal models or vision-language models (VLMs).
• Knowledge of model safety, alignment techniques, and responsible AI frameworks.
• Familiarity with LangChai

Benefits

Additional Information

About Payoneer Founded in 2005, Payoneer is the global financial platform that removes friction from doing business across borders, with a mission to connect the world's underserved businesses to a rising global economy. We're a community with over 2,500 colleagues all over the world, working to serve customers, and partners in over 190 countries and territories. By taking the complexity out of the financial workflows-including everything from global payments and compliance to multi-currency and workforce management, to providing working capital and business intelligence-we give businesses the tools they need to work efficiently worldwide and grow with confidence. Role Summary We are looking for a technically strong AI / ML Developer with hands-on expertise in training and fine-tuning Small Language Models (SLMs) and RAG Solutions. The ideal candidate will drive end-to-end AI Solutions - from dataset curation and pre-processing to training, evaluation, and production deployment. You will collaborate closely with product, product engineers, and infrastructure teams to build AI solutions that are efficient, scalable, and business-aligned.

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