Machine Learning Engineer

Position Overview

We are seeking a highly skilled

Machine Learning Engineering Consultant

to lead the design, development, deployment, and optimization of machine learning pipelines and AI-driven solutions. This role will support a new strategic initiative and focus on delivering scalable, production-ready ML systems that solve complex business challenges across multiple domains.

The ideal candidate will have strong experience in MLOps, cloud-based architectures, and end-to-end model deployment within enterprise environments.

Key Responsibilities

• Design, develop, and maintain end-to-end machine learning pipelines for advanced AI and ML models
• Build scalable and efficient ML architectures that integrate with existing enterprise systems
• Collaborate with data scientists, research teams, and software engineers to operationalize ML solutions
• Deploy, monitor, and optimize machine learning models in production environments
• Perform model tuning, prompt tuning, and performance optimization
• Evaluate and implement emerging AI/ML tools, frameworks, and technologies
• Provide technical leadership and mentorship to team members
• Partner with stakeholders to gather requirements and ensure alignment with business objectives
• Support CI/CD processes and DevOps practices for ML and application delivery
• Ensure high-quality delivery, testing, and maintenance of enterprise AI products

Required Qualifications

• Bachelor’s degree in Computer Science, Machine Learning, or related field (Master’s or Ph.D. preferred)
• 5\+ years of experience in machine learning engineering within production environments
• Strong proficiency in Python (certification preferred)
• Advanced SQL skills
• Hands-on experience with AWS cloud services, including:
• Amazon Bedrock
• Amazon SageMaker
• AWS EKS / Kubernetes
• AWS Step Functions
• Experience with Apache Airflow
• MLOps experience, including model deployment and lifecycle management
• Experience with CI/CD pipelines and DevOps tools (e.g., GitHub, Terraform, AWS CloudFormation, Gradle, Puppet)
• Experience with Docker and containerized environments
• Strong understanding of system architecture, software design principles, and scalable infrastructure
• Proven ability to independently own and deliver ML solutions end-to-end
• Strong communication and collaboration skills

Preferred Qualifications

• Experience with Apache Kafka
• Experience with MLflow
• Experience with StreamSets
• Familiarity with large-scale data processing tools (e.g., Spark, Kubeflow)
• Experience with ML/NLP frameworks and tools such as TensorFlow, PyTorch, Scikit-learn, HuggingFace, XGBoost, or LangChain
• Experience integrating ML models into enterprise systems (e.g., Flask, ElasticSearch, PostgreSQL, IBM MQ)