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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 Expertise
  • Experience 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 Engineering
  • Strong 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 & Infrastructure
  • Familiarity 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 Use
  • Frameworks: PyTorch, TensorFlow, Hugging Face Transformers
  • Serving: vLLM, TensorRT-LLM, SGlang, OpenAI API
  • Infrastructure: Docker, Kubernetes, AWS, GCP
  • Databases: PostgreSQL, Redis, Vector Databases


    • We 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. 


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