AI Engineer

Position Overview  We are hiring an AI Engineer to build, fine-tune, deploy, and scale large language model–based systems. The role focuses on LLM optimization, backend API development, and MLOps, including RAG pipelines, efficient model serving, and automated evaluation. You’ll work on taking LLMs from experimentation to production-ready, scalable AI solutions.   ShyftLabs is a growing data product company that was founded in early 2020 and works primarily with Fortune 500 companies. We deliver digital solutions built to help accelerate the growth of businesses in various industries, by focusing on creating value through innovation. 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