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.