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
