Machine Learning Engineer

Atlanta, Georgia, United States

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Not SpecifiedCompensation

Junior (1 to 2 years)Experience Level

Full TimeJob Type

UnknownVisa

Information Technology & ServicesIndustries

Requirements

Candidates should possess a Bachelor’s or Master’s degree in Computer Science, Engineering, Machine Learning, or a related quantitative field, along with 3+ years of experience in machine learning engineering or software engineering with a focus on ML infrastructure. They must be proficient in Python, SQL, and experienced with ML frameworks such as TensorFlow, PyTorch, and scikit-learn, and have hands-on experience with AWS services including SageMaker, EC2, S3, Lambda, Glue, and other ML-focused AWS offerings. Familiarity with containerization and orchestration (Docker, Kubernetes) and data processing frameworks (Spark, Pandas, Dask) is also required.

Responsibilities

The Machine Learning Engineer will design, build, and maintain highly scalable, robust, and efficient cloud infrastructure using AWS services, developing automation and orchestration of ML pipelines, and building and deploying production-ready ML models for pricing optimization, operational efficiency, and predictive analytics applications. They will also implement natural language processing solutions and conversational AI systems, collaborate with cross-functional teams, optimize data processing pipelines and AWS resources, implement monitoring and alerting strategies, and stay updated with industry trends and best practices in MLOps and machine learning infrastructure.

Skills

AWS

SageMaker

EC2

Lambda

ML pipelines

Natural Language Processing

Conversational AI

Data ingestion

Feature engineering

Model deployment

MLOps

Cloud infrastructure

Monitoring and alerting

ShyftLabs

Data-driven decision-making solutions for organizations

About ShyftLabs

ShyftLabs helps organizations adopt a data-first approach to their decision-making processes. Their services focus on establishing systems that enable companies to make quicker and more informed decisions based on data analysis. This approach allows businesses to gain insights that can keep them ahead of their competitors. Unlike other companies that may offer generic consulting services, ShyftLabs emphasizes the importance of data in driving decisions, ensuring that organizations can leverage their data effectively to enhance their strategic planning and operational efficiency.

None, CanadaHeadquarters

2018Year Founded

VENTURE_UNKNOWNCompany Stage

Data & Analytics, ConsultingIndustries

11-50Employees

Machine Learning Engineer

Requirements

Responsibilities

Skills

ShyftLabs

About ShyftLabs

Benefits

Health Insurance

Hybrid Work Options

Professional Development Budget

Risks

Increased competition from startups offering innovative, cost-effective solutions.

Growing demand for in-house analytics teams reducing reliance on consultants.

Rapid AI advancements may outpace ShyftLabs' current technology offerings.

Differentiation

ShyftLabs specializes in data governance, warehousing, and predictive analysis services.

The firm empowers organizations with a data-first approach for decision-making.

ShyftLabs establishes processes for faster, insightful decisions to outpace competition.

Upsides

Increased demand for data governance due to stricter privacy regulations.

Growing interest in predictive analytics in retail for inventory optimization.

Rising adoption of cloud-based BI tools among SMEs for cost-effectiveness.