AI & Machine Learning Job Support
AI and ML move faster than any other field in software. New model architectures, LLM APIs, vector databases — the landscape changes weekly. When your RAG pipeline returns hallucinated results or your PyTorch training loop stops converging, our AI/ML job support connects you with a senior ML engineer in real time.
AI/ML Areas We Cover
Large Language Models & Generative AI
- OpenAI / Anthropic API integration – function calling, structured outputs, streaming
- LangChain & LlamaIndex – chains, agents, retrieval, memory
- RAG (Retrieval-Augmented Generation) – embedding pipelines, chunking strategies, vector search
- Fine-tuning with LoRA / QLoRA – dataset preparation, training on A100s, PEFT
- Prompt engineering – system prompts, few-shot, chain-of-thought
Classical ML & Deep Learning
- Scikit-learn – model selection, hyperparameter tuning, feature pipelines
- PyTorch – custom datasets, training loops, mixed precision, distributed training
- TensorFlow / Keras – SavedModel, TFLite, TF Serving
- Hugging Face Transformers – fine-tuning BERT, GPT-2, T5 for NLP tasks
MLOps & Infrastructure
- MLflow – experiment tracking, model registry, serving
- Weights & Biases – sweep configuration, artefact management
- SageMaker – training jobs, endpoints, pipelines
- Vertex AI – custom training, model monitoring
- Kubeflow / Argo Workflows for ML pipelines
- Feature stores – Feast, Tecton
Vector Databases
- Pinecone, Weaviate, Qdrant, Chroma – indexing, similarity search, metadata filtering
- pgvector for PostgreSQL-based vector search
Problems We Help Solve
- Model accuracy not improving after dozens of epochs
- LLM producing inconsistent or hallucinated answers
- RAG pipeline returning irrelevant chunks
- CUDA out-of-memory during training
- ML model deployed but returning wrong predictions in production
- MLflow runs not logging correctly