Position Overview-
We are looking for an experienced AI Engineer to design, build, and optimize AI-powered applications, leveraging both traditional machine learning and large language models (LLMs). The ideal candidate will have a strong foundation in LLM fine-tuning, inference optimization, backend development, and MLOps, with the ability to deploy scalable AI systems in production environments.
ShyftLabs is a leading data and AI company, helping enterprises unlock value through AI-driven products and solutions. We specialize in data platforms, machine learning models, and AI-powered automation, offering consulting, prototyping, solution delivery, and platform scaling. Our Fortune 500 clients rely on us to transform their data into actionable insights.
Key Responsibilities:
Design and implement traditional ML and LLM-based systems and applications.Optimize model inference for performance and cost-efficiency.Fine-tune foundation models using methods like LoRA, QLoRA, and adapter layers.Develop and apply prompt engineering strategies including few-shot learning, chain-of-thought, and RAG.Build robust backend infrastructure to support AI-driven applications.Implement and manage MLOps pipelines for full AI lifecycle management.Design systems for continuous monitoring and evaluation of ML and LLM models.Create automated testing frameworks to ensure model quality and performance.,
Basic Qualifications:
Bachelor’s degree in Computer Science, AI, Data Science, or a related field.4+ years of experience in AI/ML engineering, software development, or data-driven solutions.LLM ExpertiseExperience with parameter-efficient fine-tuning (LoRA, QLoRA, adapter layers).Understanding of inference optimization techniques: quantization, pruning, caching, and serving.Skilled in prompt engineering and design, including RAG techniques.Familiarity with AI evaluation frameworks and metrics.Experience designing automated evaluation and continuous monitoring systems.Backend EngineeringStrong proficiency in Python and frameworks like FastAPI or Flask.Experience building RESTful APIs and real-time systems.Knowledge of vector databases and traditional databases.Hands-on experience with cloud platforms (AWS, GCP, Azure) focusing on ML services.MLOps & InfrastructureFamiliarity with model serving tools (vLLM, SGLang, TensorRT).Experience with Docker and Kubernetes for deploying ML workloads.Ability to build monitoring systems for performance tracking and alerting.Experience building evaluation systems using custom metrics and benchmarks.Proficient in CI/CD and automated deployment pipelines.Experience with orchestration tools like Airflow.Hands-on experience with LLM frameworks (Transformers, LangChain, LlamaIndex).Familiarity with LLM-specific monitoring tools and general ML monitoring systems.Experience with distributed training and inference on multi-GPU environments.Knowledge of model compression techniques like distillation and quantization.Experience deploying models for high-throughput, low-latency production use.Research background or strong awareness of the latest developments in LLMs.Tools & Technologies We UseFrameworks: PyTorch, TensorFlow, Hugging Face TransformersServing: vLLM, TensorRT-LLM, SGlang, OpenAI APIInfrastructure: Docker, Kubernetes, AWS, GCPDatabases: PostgreSQL, Redis, Vector DatabasesWe are proud to offer a competitive salary alongside a strong healthcare insurance and benefits package. We pride ourselves on the growth of our employees, offering extensive learning and development resources.
