Machine Learning Engineer

Overview

The Machine Learning Engineer Consultant will be responsible for leading the design, development, deployment, and monitoring of ML pipelines for advanced AI algorithms and ML models to solve complex business problems across diverse domains. Upon conversion, this role carries 10% bonus potential.

Primary Responsibilities

• Lead the design and development of ML pipelines for advanced AI algorithms and ML models to solve complex problems across diverse business domains.
• Collaborate on development of ML architecture and implement robust, efficient, and scalable AI systems that integrate seamlessly with existing infrastructure and platforms.
• Collaborate with research scientists, data scientists, and software engineers to translate research findings into practical, scalable AI solutions.
• Evaluate and experiment with emerging AI technologies, frameworks, and methodologies to stay at the forefront of innovation.
• Provide technical guidance and mentorship to junior team members, fostering a culture of continuous learning and growth.
• Collaborate with stakeholders to understand requirements, gather feedback, and iterate on AI solutions to ensure alignment with business objectives.
• Deploy, test, and optimize ML models and data pipelines in production environments.
• Perform model tuning, prompt tuning, and other ML optimization processes alongside other technical experts to maximize the mission impact of the AI product.
• Participate in the delivery, evaluation, and maintenance of enterprise products, ensuring they meet high-quality standards.

Qualifications

• Advanced degree (Master's or Ph.D.) or equivalent industry experience in Computer Science, Machine Learning, or related fields.
• 5+ years of experience in a similar role in a production environment.
• Experience working with large scale datasets and building ETL pipelines using Spark, Kubeflow, StreamSets, etc.
• Hands-on experience with cloud computing platforms such as AWS.
• Strong proficiency in Python and experience with NLP techniques, resources, and methodologies such as Scikit-learn, TensorFlow, PyTorch, HuggingFace, Comprehend, XGBoost, LangChain, etc.
• Experience integrating machine learning models and data-driven algorithms into larger system architectures that involve pieces like Flask, ElasticSearch, PostgreSQL, IBM MQ, Apache Kafka, etc.
• Experience with iterative development processes, thriving in dynamic and agile environments.
• Ability to own ML delivery tasks end-to-end with little to no direct support.
• Hands-on experience in deploying machine learning models into production environments.
• Strong understanding of software design patterns, principles, architecture, and operations.
• Strong communication skills and the ability to collaborate effectively with business partners, vendors, end users, and cross-functional teams.

Physical Job Requirements

• Ability to utilize keyboard, mouse, and computer for up to 8 hours per day.
• Ability to work at least 40 hours per week.

Technical Skills

Must Have

• Advanced SQL
• Amazon AWS Cloud
• Amazon Bedrock
• Amazon SageMaker
• Apache Airflow
• AWS EKS / Kubernetes
• AWS Step Functions
• Certified Python programmer
• CI/CD deployment
• DevOps pipeline experience related to the automation of application testing, delivery, and infrastructure as code (e.g., GitHub, Gradle, Puppet, Terraform, AWS CloudFormation)
• Docker for AWS
• MLOps

Nice To Have

• Apache Kafka
• MLFlow
• StreamSets

Additional Requirements

• A candidate must have a Python certification.
• Need good communicator as well as must have all the skills mentioned below-
• The manager really wants someone that is really polished and higher level within AI (I know its newer but higher level than just entry level)
• LOOKING FOR LOCAL CANDIDATES FROM MN ONLY
• In-Person Interview