AI Engineer

Job 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.

Key Responsibilities

AI System Development

·       Build and maintain production AI pipelines and supporting infrastructure.

·       Develop RAG systems, embeddings pipelines, and context-engineering layers.

·       Implement scalable model-serving, orchestration, and automation processes.

Model Engineering & Optimization

·       Perform model selection, fine-tuning, and optimization for various use cases.

·       Conduct advanced prompt engineering for LLM-based systems.

·       Run model experiments, diagnostics, and performance tuning.

Evaluation & Quality Assurance

·       Develop evaluation datasets and rigorous testing frameworks.

·       Validate model quality, accuracy, and consistency through experimentation.

·       Ensure models meet production-level reliability and performance standards.

Deployment & Operations

·       Collaborate with DevOps/MLOps teams to deploy and maintain AI models.

·       Implement monitoring, observability, and error-handling mechanisms.

·       Ensure scalability, operational efficiency, and compliance.

Qualifications & Requirements

·       Bachelor’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.