AI Engineer

Job Responsibilties:

• 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

Basic Qualifications:

• 4–8 years of relevant experience in LLMs, Backend Engineering, and MLOps.
• LLM Expertise
• Model Fine-tuning: Experience with parameter-efficient fine-tuning methods (LoRA, QLoRA, adapter layers)
• Inference Optimization: Knowledge of quantization, pruning, caching strategies, and serving optimizations
• Prompt Engineering: Prompt design, few-shot learning, chain-of-thought prompting, and retrieval-augmented generation (RAG)
• Model Evaluation: Experience with AI evaluation frameworks and metrics for different use cases
• Monitoring & Testing: Design of automated evaluation pipelines, A/B testing for models, and continuous monitoring systems
• Backend Engineering
• Languages: Proficiency in Python, with experience in FastAPI, Flask, or similar frameworks
• APIs: Design and implementation of RESTful APIs and real-time systems
• Databases: Experience with vector databases and traditional databases
• Cloud Platforms: AWS, GCP, or Azure with focus on ML services
• MLOps & Infrastructure
• Deployment: Experience with model serving frameworks (vLLM, SGLang, TensorRT)
• Containerization: Docker and Kubernetes for ML workloads
• Monitoring: ML model monitoring, performance tracking, and alerting systems
• Evaluation Systems: Building automated evaluation pipelines with custom metrics and benchmarks
• CI/CD: MLOps pipelines for automated testing, and deployment
• Orchestration: Experience with workflow tools like Airflow.

Preferred Qualifications:

• LLM Frameworks: Hands-on experience with Transformers, LangChain, LlamaIndex, or similar
• Monitoring Platforms: Knowledge of LLM-specific monitoring tools and general ML monitoring
• Distributed Training and Inference: Experience with multi-GPU and distributed training and inference setups
• Model Compression: Knowledge of techniques like distillation, quantization, and efficient architectures
• Production Scale: Experience deploying models handling high-throughput, low-latency requirements
• Research Background: Familiarity with recent LLM research and ability to implement novel techniques
• Tools & Technologies We Use
• Frameworks: PyTorch, Transformers, TensorFlow
• Serving: vLLM, TensorRT-LLM, SGlang,  OpenAI API,
•  Infrastructure: Kubernetes, Docker, AWS/GCP
• Databases: PostgreSQL, Redis, Vector DBs