AI Engineer

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

• Large Language Models (LLMs)
• Python
• Model Fine-tuning (LoRA, QLoRA)
• Inference Optimization
• Prompt Engineering
• RAG (Retrieval-Augmented Generation)
• FastAPI
• Flask
• RESTful API Design
• Vector Databases
• AWS
• GCP
• Azure
• Docker
• Kubernetes
• vLLM
• SGLang
• TensorRT
• MLOps
• CI/CD
• Airflow
• Model Evaluation Frameworks
• A/B Testing
• PostgreSQL
• Redis
• 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
• AWS, GCP, or Azure with focus on ML services
• 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
• CI/CD: MLOps pipelines for automated testing, and deployment
• Experience with workflow tools like Airflow

Preferred Skills

Benefits

• Competitive salary
• Strong insurance package
• Extensive learning and development resources