AI / ML Engineer
DashMindsIQ builds AI systems for clients, RAG pipelines, AI agents, LLM integrations, custom ML models, and the evaluation and monitoring infrastructure that keeps them working in production. We are looking for an AI/ML Engineer who has moved beyond running Jupyter notebooks and can design, build, and deploy AI systems that other people's businesses depend on.
You will work across the full AI delivery lifecycle, from understanding the business problem, designing the architecture, writing production-quality Python, building evaluation pipelines, and deploying to cloud infrastructure. Breadth matters here: we need engineers who can integrate an LLM, train a classification model, and set up an MLflow tracking server, not specialists who can do one and not the others.
Design and build RAG systems: document ingestion pipelines, chunking and embedding strategies, vector database configuration (Pinecone, Weaviate, or pgvector), hybrid retrieval, and response generation with source citation
Integrate large language models (Claude, GPT-4o, Gemini, Llama) into client applications, handling prompt engineering, context management, output parsing, cost monitoring, and fallback logic
Build AI agent systems using LangGraph, LlamaIndex, or AutoGen, defining tool sets, memory architecture, orchestration logic, and the human-in-the-loop checkpoints that keep autonomous systems safe
Develop custom ML models for classification, regression, time-series forecasting, and anomaly detection using PyTorch or Scikit-learn, with full MLOps pipelines in MLflow
Build evaluation frameworks for AI systems, automated test suites that assess LLM output accuracy, RAG retrieval quality (using RAGAS), and model performance on held-out data
Deploy AI systems to production on cloud infrastructure, FastAPI serving layers, containerised model inference, async job processing for batch workloads, and monitoring with Langfuse or Arize
Work directly with clients to understand what they are trying to achieve and translate that into a system design, not just implement a specification someone else wrote
Monitor deployed AI systems for drift, latency, hallucination, and cost, and improve them iteratively based on real usage rather than leaving them static after launch
2–4 years of hands-on AI/ML engineering experience, we want to see code you have written and systems you have deployed, not just academic or Kaggle experience
Strong Python engineering: you write clean, testable, documented code, not just scripts that work on your laptop
Hands-on LLM integration experience: prompt engineering, context window management, structured output extraction, and handling model API errors and rate limits in production
RAG pipeline experience: you have built at least one end-to-end RAG system and can explain the trade-offs between chunking strategies, embedding models, and retrieval approaches
Familiarity with MLOps concepts: experiment tracking, model versioning, deployment pipelines, and drift monitoring, you do not need to have built all of these from scratch, but you need to understand why they matter
Good written communication: you can explain a model's behaviour, a RAG pipeline's failure mode, or an architectural trade-off clearly in writing, to a technical colleague or a non-technical client
Fill in the quick form and attach your resume. We’ll take it from there.