The Role
Build, fine-tune, optimize, deploy, and scale traditional machine learning and LLM systems. Responsibilities include prompt engineering, RAG pipelines, backend APIs, inference optimization, model serving, MLOps, automated evaluation, monitoring, testing, A/B experimentation, and production infrastructure. The role requires Python, cloud platforms, containerization, Kubernetes, model-serving frameworks, vector databases, and experience with parameter-efficient fine-tuning, model optimization, and scalable AI applications.
Summary Generated by Built In
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
Skills Required
- Large language model experience
- Python proficiency
- Parameter-efficient fine-tuning using LoRA, QLoRA, or adapter layers
- Inference optimization, quantization, pruning, caching, and model-serving optimization
- Prompt engineering, few-shot learning, chain-of-thought prompting, and RAG
- FastAPI, Flask, or similar backend frameworks
- RESTful API and real-time systems development
- Vector databases and traditional databases
- Experience with AWS, GCP, or Azure ML services
- vLLM, SGLang, or TensorRT model-serving frameworks
- Docker and Kubernetes for machine learning workloads
- ML model monitoring, performance tracking, and alerting
- Automated model evaluation pipelines, metrics, benchmarks, and A/B testing
- CI/CD and MLOps pipelines for automated testing and deployment
- Airflow or similar workflow orchestration tools
- 4–8 years of relevant LLM, backend engineering, and MLOps experience
- PyTorch
- Transformers
- TensorFlow
- LangChain
- LlamaIndex
- LLM-specific monitoring tools
- Distributed training and multi-GPU systems
- Model compression, distillation, and quantization
- High-throughput and low-latency systems
- LLM research experience
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The Company
What We Do
Simfluent builds and operates global capability centers for modern enterprises. It creates fully integrated hubs that function as permanent extensions of clients’ businesses, with capabilities spanning engineering and platforms, cloud, architecture, product, data, and AI. Through greenfield, build-operate-transfer, and hybrid models, Simfluent helps organizations scale technology delivery while preserving quality, culture, strategic alignment, and local control, with predictable execution at enterprise scale.
