Senior/Lead MLOps Engineer – AI and Generative AI

Description

Job Summary: We are seeking an MLOps Engineer to implement and industrialize ML and Generative AI models, with a focus on scalable AI architectures leveraging LLMs and RAG. Key Highlights: 1. Implementation and industrialization of ML and Generative AI models 2. Designing scalable AI architectures focused on LLMs and RAG 3. Development of robust MLOps pipelines We are looking for an MLOps Engineer with experience implementing and industrializing Machine Learning and Generative Artificial Intelligence models in production environments. The candidate will be responsible for designing scalable AI architectures, with emphasis on LLMs, RAG (Retrieval\-Augmented Generation), autonomous agents, and robust MLOps pipelines. **Responsibilities** \- Deploy, version, and monitor ML models in production \- Develop MLOps pipelines (training, validation, deployment, and monitoring) \- Implement solutions based on LLMs (OpenAI, Llama, Mistral, or similar) \- Design RAG architectures using vector databases \- Develop and orchestrate AI agents \- Implement fine\-tuning and model optimization \- Manage embeddings and vector databases (Pinecone, Weaviate, FAISS, Milvus) \- Implement APIs for model exposure (FastAPI, Flask) \- Monitor performance, drift, costs, and response quality \- Ensure scalability, governance, and security of AI solutions **Requirements** \- Degree in Computer Science, Engineering, or related fields \- Advanced Python \- Experience with ML frameworks (TensorFlow, PyTorch, Scikit\-Learn) \- Experience with Docker and Kubernetes \- CI/CD implementation for ML \- Cloud experience (AWS, GCP, or Azure) \- Practical experience with LLMs in production \- Experience with RAG and vector databases \- Building and deploying APIs for AI models \- Implementing AI observability and monitoring (hallucination tracking, prompt quality) **For Senior Level:** \- Experience in scalable AI architecture design \- Experience with agent orchestration frameworks (LangChain, CrewAI, Semantic Kernel) \- Experience with model serving (MLflow, Seldon, KServe) \- Strategies for inference optimization and cost reduction **Nice-to-Haves** \- Postgraduate degree or specialization in AI/Data (advantageous) \- Experience with Generative AI in enterprise environments \- LLM fine\-tuning \- Advanced prompt engineering \- Microservices experience \- Experience with data pipelines (Airflow, Kafka) Employment Type: Full-time CLT or Freelance/PJ Benefits: * Meal allowance * Transportation allowance Work Location: On-site