AI Engineer (Mid‑Level) - 3 to 6 years at NielsenIQ

About the Role

We are looking for a Mid-Level AI Engineer to join our team. This role focuses on building secure, scalable, and production-ready AI systems.

Key Responsibilities

• →Build and optimize machine learning and LLM-based models (classification, NLP, embeddings, generative AI).
• →Develop reusable model pipelines in Python using libraries like scikit-learn, PyTorch, TensorFlow, or HuggingFace.
• →Implement feature engineering, vectorization, and model evaluation frameworks.
• →Fine-tune, evaluate, and deploy LLMs using frameworks such as HuggingFace Transformers, LangChain, LlamaIndex, or cloud-native AI services.
• →Build RAG (Retrieval-Augmented Generation) pipelines using vector databases (Pinecone/Weaviate/FAISS/Snowflake Cortex Search).
• →Implement prompt engineering, prompt orchestration, and prompt quality testing.
• →Build automated training, deployment, and monitoring pipelines using CI/CD (GitHub Actions/Azure DevOps/GitLab CI).
• →Manage model versioning, lineage, and artifact tracking (MLflow, Weights & Biases).
• →Implement reproducibility, environment management, and dependency controls.
• →Deploy AI services to AWS/Azure/GCP using serverless compute or containerized microservices (Lambda, Azure Functions, Cloud Run, Kubernetes).
• →Optimize compute workloads, costs, and auto-scaling strategies.
• →Integrate models into production systems via APIs, SDKs, or event-driven flows.
• →Apply DevSecOps practices: secure model endpoints, manage secrets, enforce least-privilege IAM.
• →Implement monitoring for drift, model decay, model failures, and performance anomalies.
• →Contribute to logging, metrics, dashboards, and alerting for AI workloads.
• →Partner with Data Engineering, Product, and Analytics teams to integrate models into applications and data pipelines.
• →Write clear documentation (model cards, evaluation reports, design documents, runbooks).