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Mid LevelFull-timePythonAWSGCPAzureKubernetesDockerPostgreSQLRedisTensorFlowPyTorch
Salary Range
$102,000 - $138,000 /year
This salary is estimated based on similar roles. The actual salary may vary.
About the Role
We are looking for an AI Engineer to build, fine-tune, deploy, and scale large language model-based systems. This role focuses on LLM optimization, backend API development, and MLOps, including RAG pipelines, efficient model serving, and automated evaluation.
Key Responsibilities
- →Design and implement traditional ML and LLM-based systems and applications
- →Optimize model inference performance and cost efficiency
- →Fine-tune foundation models for specific use cases and domains
- →Implement diverse prompt engineering strategies
- →Build robust backend infrastructure for AI-powered applications
- →Implement and maintain MLOps pipelines for AI lifecycle management
- →Design and implement comprehensive traditional ML and LLM monitoring and evaluation systems
- →Develop automated testing frameworks for model quality and performance tracking
Requirements
- ✓4–8 years of relevant experience in LLMs, Backend Engineering, and MLOps
- ✓Experience with parameter-efficient fine-tuning methods (LoRA, QLoRA, adapter layers)
- ✓Knowledge of quantization, pruning, caching strategies, and serving optimizations
- ✓Prompt design, few-shot learning, chain-of-thought prompting, and retrieval-augmented generation (RAG)
- ✓Experience with AI evaluation frameworks and metrics for different use cases
- ✓Design of automated evaluation pipelines, A/B testing for models, and continuous monitoring systems
- ✓Proficiency in Python, with experience in FastAPI, Flask, or similar frameworks
- ✓Design and implementation of RESTful APIs and real-time systems
- ✓Experience with vector databases and traditional databases
- ✓Experience with model serving frameworks (vLLM, SGLang, TensorRT)
- ✓Docker and Kubernetes for ML workloads
- ✓ML model monitoring, performance tracking, and alerting systems
- ✓Building automated evaluation pipelines with custom metrics and benchmarks
- ✓MLOps pipelines for automated testing, and deployment
- ✓Experience with workflow tools like Airflow
Benefits
- ★Competitive salary and strong insurance package
- ★Extensive learning and development resources
