AI, ML & Data Science Proxy Job Support
Artificial intelligence and machine learning have moved from research labs into production systems at extraordinary speed. Data scientists and ML engineers are now expected to deploy models, build data pipelines, evaluate LLMs, and integrate AI into applications — all while keeping up with a field that changes monthly.
Our AI/ML and data science proxy job support gives you live access to senior ML engineers and data scientists who work on these problems every day.
Generative AI & LLM Support
The generative AI landscape is moving faster than any documentation can keep up with. We help with:
LLM Integration
- OpenAI, Anthropic, and open-source LLMs (Llama 3, Mistral, Gemma)
- Prompt engineering – system prompt design, few-shot examples, output formatting
- Function calling / tool use – structured outputs, JSON mode
- Token optimisation – reducing costs while maintaining quality
RAG (Retrieval-Augmented Generation)
- Document processing – chunking strategies, overlap, metadata extraction
- Embedding models – text-embedding-3-small, BGE, E5
- Vector databases – Pinecone, Weaviate, Chroma, pgvector indexing
- Retrieval quality – hybrid search, re-ranking with cross-encoders
- Hallucination reduction – grounding, citation, confidence scoring
LLM Frameworks
- LangChain – LCEL chains, agents, memory, tools
- LlamaIndex – query engines, node postprocessors, structured extraction
- Haystack, Semantic Kernel
Classical ML & Deep Learning
Machine Learning
- Feature engineering – encoding, scaling, imputation, interaction terms
- Model selection – train/validation/test split, cross-validation, hyperparameter tuning
- Scikit-learn pipelines – ColumnTransformer, custom transformers, GridSearchCV
- Gradient boosting – XGBoost, LightGBM, CatBoost – parameter tuning, SHAP analysis
- Model evaluation – precision/recall trade-offs, ROC-AUC, confusion matrix interpretation
Deep Learning
- PyTorch – custom datasets, DataLoader, training loops, mixed precision (AMP)
- Fine-tuning pre-trained models – transfer learning, LoRA, QLoRA with PEFT
- Hugging Face Transformers – tokenizers, model hub, pipelines, ONNX export
- Computer vision – torchvision, YOLO integration, image augmentation
MLOps & Deployment
Experiment Tracking
- MLflow – run logging, model registry, serving with mlflow models serve
- Weights & Biases – sweep configuration, artefact management, reports
Model Serving
- FastAPI-based model serving endpoints
- TorchServe, TensorFlow Serving
- BentoML for packaging and deployment
- AWS SageMaker endpoints, Azure ML endpoints, Vertex AI
Pipelines & Orchestration
- Kubeflow Pipelines – component authoring, pipeline compilation
- Metaflow, Prefect, ZenML for ML workflow management
Data Science Support
- Exploratory data analysis – Pandas profiling, distribution analysis, outlier detection
- Statistical testing – hypothesis tests, p-values, A/B test analysis
- Time series – ARIMA, Prophet, LSTM for forecasting
- Jupyter notebooks – structuring production-quality notebooks, papermill parameterisation
- SQL for data science – window functions, cohort analysis, funnel queries
Who We Help
- Data scientists facing a deadline on model delivery
- ML engineers deploying models to production for the first time
- Developers adding AI/LLM features to existing applications
- Researchers transitioning to industry ML roles