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Job Description
- Design, train, evaluate and deploy ML and deep learning models for real business problems.
- Build Generative AI applications using LLMs, including RAG pipelines, AI agents, prompt engineering and fine-tuning.
- Work with frameworks such as LangChain, LlamaIndex and vector databases (Pinecone, FAISS, Weaviate, pgvector).
- Develop data preprocessing, feature engineering and model training pipelines.
- Deploy models as scalable APIs and services; set up MLOps practices for versioning, monitoring and retraining.
- Evaluate model performance, accuracy, latency and cost, and optimise accordingly.
- Work with product, data and engineering teams to take AI features from proof of concept to production.
- 5+ years in ML/AI engineering or data science with production deployments.
- Strong Python with scikit-learn, PyTorch or TensorFlow.
- Hands-on experience with LLMs (OpenAI, Anthropic Claude, Llama, Mistral) and RAG architectures.
- Experience with NLP, computer vision or predictive modelling.
- MLOps tools such as MLflow, Kubeflow, SageMaker, Azure ML or Vertex AI.
- Experience with Docker, APIs (FastAPI/Flask) and cloud platforms.
- Model fine-tuning (LoRA/PEFT) and LLM evaluation frameworks.
- Agentic AI frameworks (LangGraph, CrewAI, AutoGen) and MCP.
- Experience with Spark or Databricks for large-scale data.
- Publications, Kaggle work or open-source contributions.
Skills
PythonFlaskFastAPIMachine LearningDeep LearningNLPComputer VisionTensorFlowPyTorchScikit-learnLangChainLLMGenerative AIPrompt EngineeringRAGIf a job posting appears fraudulent, asks for payment, contains misleading information, or violates our guidelines, please report it immediately. Our team will review it promptly, Jobaaj does not charge any fee from the applicants.
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