ML Engineer

Position Overview:
Here at ShyftLabs, we are searching for an experienced ML Engineer who can derive performance improvement and cost efficiency in our product through a deep understanding of the ML/AI and infra system, and provide a data driven and scientific solution that utilize Cloud AI services with proper cloud application pipeline with production ready quality.

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:

• Develop and deploy AI solutions while designing and managing the infrastructure needed using Natural Language Processing(NLP), Gen-AI and ML Techniques.
• Collaborate with cross-functional teams to identify opportunities and integrate AI solutions with proper application pipeline and production ready quality.
• Strong understanding of cloud-based AI solutions and application pipelines.
• Collaborate with data scientists and engineers to ensure data quality and accessibility.
• Design, implement, and optimize machine learning algorithms for tasks like classification, recommendation, prediction, and clustering.
• Develop and maintain robust AI infrastructure.
• Document technical designs, decisions, and processes, and communicate progress and results to stakeholders.
• Work with cross-functional teams to integrate AI/ML models into production-level applications.

Basic Qualifications:

• Bachelor's degree with 4+ years of professional experience or Master's Degree with 5+ year of professional experience in a quantitative discipline or equivalent.
• Distinctive problem-solving skills, good at articulating product questions, pulling data from large datasets, and using statistics to arrive at a recommendation.
• Ability to build positive relationships within ShyftLabs and with our stakeholders, and work effectively with cross-functional partners in a global company.
• Statistics: must have strong knowledge and experience in experimental design, hypothesis testing, and various statistical analysis techniques.
• ML/AI: must have a deep understanding of ML algorithms, NLP and Generative AI.
• MLOps: must have a strong understanding of ML and LLM Ops.
• Cloud: strong understanding of cloud architecture and Cloud AI solutions.
• Programming: experience with Python in relevant libraries(ex - SKlearn, pandas, LangChain, etc) and SQL.