Machine Learning Engineering Consultant

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.

• Bachelor’s degree or higher in Computer Science, Machine Learning, or a related field, or equivalent industry experience.
• 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, Stream Sets, 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, Tensor Flow, PyTorch, Hugging Face, Comprehend, XGBoost, Lang Chain, etc.
• Experience integrating machine learning models and data‑driven algorithms into larger system architectures that involve pieces like Flask, Elastic Search, 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.