LLM AI Engineer

As an Ops Engineer at Gainwell, you will play a crucial role in the AI/ML team by developing, deploying, and maintaining scalable infrastructure and pipelines for Machine Learning models and Large Language Models (LLMs). Your responsibilities will include collaborating closely with Data Scientists and DevOps to ensure smooth model lifecycle management, performance monitoring, version control, and compliance. You will have core responsibilities for LLM Ops and ML Ops: **Key Responsibilities:** **Core LLM Ops Responsibilities:** - Develop and manage scalable deployment strategies tailored for LLMs (GPT, Llama, Claude, etc.). - Optimize LLM inference performance, including model parallelization, quantization, pruning, and fine-tuning pipelines. - Integrate prompt management, version control, and retrieval-augmented generation (RAG) pipelines. - Manage vector databases, embedding stores, and document stores used with LLMs. - Monitor hallucination rates, token usage, and cost optimization for LLM APIs or on-prem deployments. - Continuously monitor models" performance and ensure alert systems are in place. - Ensure compliance with ethical AI practices, privacy regulations, and responsible AI guidelines. **Core ML Ops Responsibilities:** - Design, build, and maintain robust CI/CD pipelines for ML model training, validation, deployment, and monitoring. - Implement version control, model registry, and reproducibility strategies for ML models. - Automate data ingestion, feature engineering, and model retraining workflows. - Monitor model performance, drift, and ensure proper alerting systems. - Implement security, compliance, and governance protocols for model deployment. - Collaborate with Data Scientists to streamline model development and experimentation. **Qualification Required:** - Bachelor's or Master's degree in Computer Science, Data Sciences-Machine Learning, Engineering, or related fields. - Strong experience with ML Ops tools (Kubeflow, ML flow, TFX, Sage Maker, etc.). - Experience with LLM-specific tools and frameworks. - Proficient in deploying models in cloud (AWS, Azure, GCP) and on-prem environments. - Strong coding skills in Python, Bash, and familiarity with infrastructure-as-code tools. - Knowledge of healthcare AI applications and regulatory compliance.,