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 will 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 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

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

We are proud to offer a competitive salary alongside a strong insurance package. We pride ourselves on the growth of our employees, offering extensive learning and development resources.