What are the responsibilities and job description for the Machine Learning Engineer position at Netfor, Inc.?
Overview
The Machine Learning Engineer plays a critical role in advancing Netfor’s AI and data initiatives by developing and deploying production-grade ML models within our AWS-based infrastructure. This position focuses on implementing retrieval-augmented generation (RAG) for virtual agents, optimizing MLOps pipelines, and ensuring scalability and efficiency in AI-driven workflows. The ML Engineer collaborates with Data Engineers, Software Developers, and Business Leaders to enhance Netfor’s data intelligence capabilities, supporting key initiatives like the OLTP/OLAP environment, data governance, and AI-powered automation.
Key Responsibilities
Technical Skills
Reporting
This role reports to the Director of Data Operations and collaborates closely with the Data Engineering and Software Development teams.
Work Environment
Minimal travel, primarily local and during business hours.
Other Duties
This job description is not exhaustive and may evolve with business needs. Employees may be required to perform additional duties as needed.
Top 5 required tech and soft skills:
Technical Skills (Must-have competencies)
This job description is a summary of the primary duties and responsibilities of the job and position. It is not intended to be a comprehensive or all-inclusive listing of duties and responsibilities.
Netfor, Inc. is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.
Netfor, Inc. participates in E-Verify.
Netfor, Inc. will not sponsor applicants for work visas.
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The Machine Learning Engineer plays a critical role in advancing Netfor’s AI and data initiatives by developing and deploying production-grade ML models within our AWS-based infrastructure. This position focuses on implementing retrieval-augmented generation (RAG) for virtual agents, optimizing MLOps pipelines, and ensuring scalability and efficiency in AI-driven workflows. The ML Engineer collaborates with Data Engineers, Software Developers, and Business Leaders to enhance Netfor’s data intelligence capabilities, supporting key initiatives like the OLTP/OLAP environment, data governance, and AI-powered automation.
Key Responsibilities
- ML Model Development & Deployment
- Design, develop, and deploy ML models for natural language processing (NLP), large language models (LLMs), and predictive analytics
- Implement retrieval-augmented generation (RAG) methodologies for virtual call center agents using vector databases and embeddings
- Optimize model performance for low latency and cost efficiency in production environments
- MLOps & Cloud Deployment
- Deploy and manage models using AWS services such as SageMaker, Bedrock, Lambda, ECS, Fargate, and Step Functions
- Automate model retraining, monitoring, and deployment pipelines using CI/CD (GitHub Actions, AWS CodePipeline)
- Develop scalable feature engineering and data preprocessing pipelines using AWS Glue, Athena, and Redshift
- Data Integration & Engineering
- Collaborate with Data Engineers to integrate ML workflows with OLAP and OLTP data environments
- Implement efficient data retrieval, transformation, and ingestion processes from structured and unstructured sources
- Work with dbt, SQL, and Python to support analytics and model training data preparation
- AI Governance & Model Monitoring
- Establish model versioning, logging, and performance tracking frameworks
- Ensure compliance with AI ethics, bias mitigation, and governance policies
- Work with Data Governance initiatives to document and maintain AI/ML assets
- Collaboration & Stakeholder Engagement
- Partner with business leaders and service delivery teams to align ML initiatives with strategic goals
- Educate internal teams on ML best practices, model interpretability, and AI-driven decision-making
- Provide technical leadership on emerging AI trends, tools, and methodologies
Technical Skills
- Proven experience deploying at least one production ML product (LLMs, NLP, predictive modeling, etc.)
- 3 years of experience in ML engineering, AI research, or data science
- AWS Expertise – experience with at least two or more AWS services (SageMaker, Bedrock, Lambda, Glue, Athena, Redshift, ECS, Fargate)
- Strong programming skills in Python (TensorFlow, PyTorch, Scikit-learn) and SQL. MLOps experience – CI/CD, model monitoring, data pipelines, and inference optimization
- Knowledge of retrieval-augmented generation (RAG) and vector database integration.
- Familiarity with API development for model serving and real-time inference
- Strong problem-solving and analytical skills with a focus on production-scale ML solutions
- Excellent communication and documentation skills for collaboration with technical and non-technical stakeholders.Proactive approach to learning and innovation in AI and cloud computing
- Model Performance: Ensure production models meet 99% uptime and SLA requirements
- Scalability: Optimize ML workloads to reduce cost and improve efficiency
- AI Governance: Maintain compliance with internal and external AI policies
- Stakeholder Engagement: Positive feedback from internal teams on ML solutions.
- Innovation Contribution: Implementation of at least two new AI-driven enhancements annually
Reporting
This role reports to the Director of Data Operations and collaborates closely with the Data Engineering and Software Development teams.
Work Environment
- Remote-friendly position with occasional in-office collaboration
- Requires the ability to work in a fast-paced, cloud-first environment
- Low to moderate noise level in work setting
- Full-time work-from-home position
- May require occasional travel, overtime, and weekend hours for major deployments
Minimal travel, primarily local and during business hours.
Other Duties
This job description is not exhaustive and may evolve with business needs. Employees may be required to perform additional duties as needed.
Top 5 required tech and soft skills:
Technical Skills (Must-have competencies)
- Machine Learning Model Development – Experience developing and deploying ML models, particularly in NLP, LLMs, and predictive analytics
- AWS ML & Cloud Expertise – Hands-on experience with AWS SageMaker, Bedrock, Lambda, Glue, Athena, and Redshift for model training, deployment, and inference
- MLOps & Automation – Strong knowledge of CI/CD (GitHub Actions, AWS CodePipeline), model monitoring, and automated retraining pipelines
- Retrieval-Augmented Generation (RAG) & Vector Databases – Practical experience working with RAG methodologies, embeddings, and vector database integration
- Python & SQL Proficiency – Strong coding ability in Python (TensorFlow, PyTorch, Scikit-learn) and SQL for data manipulation and feature engineering
- Problem-Solving & Analytical Thinking – Ability to optimize models, troubleshoot performance issues, and innovate AI-driven solutions
- Communication & Stakeholder Engagement – Effectively collaborates with data engineers, business leaders, and service teams to align ML solutions with company goals
- Proactive Learning & Adaptability – Stays ahead of AI advancements, adapts to evolving ML tools, and continuously improves workflows
- AI Governance & Ethical Considerations – Awareness of AI bias mitigation, compliance, and responsible AI practices
- Leadership & Knowledge Sharing – Capable of mentoring teams, documenting processes, and presenting complex ML concepts to non-technical audiences
This job description is a summary of the primary duties and responsibilities of the job and position. It is not intended to be a comprehensive or all-inclusive listing of duties and responsibilities.
Netfor, Inc. is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.
Netfor, Inc. participates in E-Verify.
Netfor, Inc. will not sponsor applicants for work visas.
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