AI Engineer

OverviewJob Purpose The AI Engineer builds production-grade AI systems including RAG pipelines, fine-tuned models, prompt engineering, model evaluation, and scalable pipelines for enterprise deployment.ResponsibilitiesAI System Development: Build and maintain production AI pipelines and supporting infrastructure.AI System Development: Develop RAG systems, embeddings pipelines, and context-engineering layers.AI System Development: Implement scalable model-serving, orchestration, and automation processes.Model Engineering & Optimization: Perform model selection, fine-tuning, and optimization for various use cases.Model Engineering & Optimization: Conduct advanced prompt engineering for LLM-based systems.Model Engineering & Optimization: Run model experiments, diagnostics, and performance tuning.Evaluation & Quality Assurance: Develop evaluation datasets and rigorous testing frameworks.Evaluation & Quality Assurance: Validate model quality, accuracy, and consistency through experimentation.Evaluation & Quality Assurance: Ensure models meet production-level reliability and performance standards.Deployment & Operations: Collaborate with DevOps/MLOps teams to deploy and maintain AI models.Deployment & Operations: Implement monitoring, observability, and error-handling mechanisms.Deployment & Operations: Ensure scalability, operational efficiency, and compliance.Qualifications & RequirementsBachelor’s degree in Computer Science, AI/ML, Data Science, Software Engineering, or related field.4–7 years of experience in AI/ML engineering, applied machine learning, or similar roles.Hands-on experience building production AI pipelines.Strong Python skills and familiarity with ML frameworks (TensorFlow, PyTorch, etc.).Knowledge of vector databases, RAG frameworks, and LLM orchestration.Experience with CI/CD, MLOps, cloud environments, and scalable infrastructure.Experience with LLM fine-tuning, evaluation, and advanced prompt engineering.Experience in enterprise or government-level AI deployments.#J-18808-Ljbffr