Role 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.
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
Preferred Skills
PyTorch
Transformers
TensorFlow
LangChain
LlamaIndex
LLM-specific Monitoring Tools
Distributed Training
Multi-GPU Setup
Model Compression
Model Distillation
Quantization
High-throughput Systems
Low-latency Systems
LLM Research
Qualifications
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
Benefits
Competitive salary
Strong insurance package
Extensive learning and development resources
