| Job Description | Role Overview We are looking for a seasoned AI/ML & MLOps Engineer to lead the development, deployment, and scaling of our machine learning initiatives. You will bridge the gap between data science and production engineering, ensuring our models ranging from traditional predictive analytics to cutting-edge Generative AI are robust, scalable, and high-performing. The ideal candidate doesn't just build models in a vacuum but builds the automated "foundries" that keep them running. Key Responsibilities - Model Development: Design, train, and optimize ML models using frameworks like PyTorch or TensorFlow.
- GenAI Implementation: Lead the integration of LLMs, including fine-tuning, prompt engineering, and building RAG (Retrieval-Augmented Generation) pipelines.
- Infrastructure & Orchestration: Architect and maintain end-to-end ML pipelines (CI/CD for ML) using Docker, Kubernetes, and tools like MLflow or Kubeflow.
- Cloud Deployment: Deploy and manage production workloads on cloud platforms (AWS/Google Cloud Platform/Azure) with a focus on cost-efficiency and low latency.
- Monitoring & Governance: Implement robust monitoring for model drift, data quality, and performance metrics to ensure 24/7 reliability.
- Collaboration: Work closely with Data Scientists to productize research and with DevOps to align with enterprise security and infrastructure standards.
Technical Requirements - Experience: 4+ years of hands-on experience in ML Engineering or MLOps roles.
- Core Stack: Expert-level proficiency in Python and standard ML libraries (Scikit-learn, Pandas, NumPy).
- Deep Learning: Strong experience with Transformers, CNNs, or RNNs.
- DevOps for ML: Mastery of containerization (Docker) and orchestration (K8s). Experience with Infrastructure as Code (Terraform/CloudFormation) is a major plus.
- GenAI Tools: Familiarity with LangChain, LlamaIndex, or Vector Databases (Pinecone, Milvus, Weaviate).
- Education: B.S./M.S. in Computer Science, Mathematics, or a related quantitative field.
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