Senior Machine Learning Engineer
📍 Location: Raleigh, North Carolina, United States (Hybrid)
🏢 Industry: IT Services and IT Consulting
💼 Work Setting: Hybrid
Are you passionate about building and scaling cutting-edge AI and Machine Learning solutions that power real-world products and drive innovation?
Join a forward-thinking technology team where you will play a key role in designing, implementing, and scaling production-grade AI systems. This is an exciting opportunity for an experienced Machine Learning Engineer who enjoys solving complex engineering challenges, leading technical initiatives, and enabling the successful deployment of advanced ML and Generative AI solutions at scale.
Key Responsibilities
Machine Learning & AI Engineering
Design, develop, and deploy scalable Machine Learning and Generative AI systems in production environments.
Build and operationalize advanced AI applications, including Retrieval-Augmented Generation (RAG) solutions and agent-based workflows.
Develop intelligent search capabilities by implementing hybrid search architectures that combine semantic and keyword-based retrieval techniques.
Transform research prototypes and data science models into robust, customer-facing applications.
Drive the adoption of best practices for AI solution architecture, deployment, monitoring, and lifecycle management.
Platform & Infrastructure Development
Architect and maintain highly scalable APIs, microservices, and model-serving infrastructure.
Build data pipelines and streaming frameworks to support large-scale data processing and analytics workloads.
Develop reusable frameworks, libraries, and engineering standards that enable AI development across multiple teams.
Optimize systems for performance, scalability, reliability, availability, and cost efficiency.
Establish and maintain CI/CD pipelines, observability frameworks, and operational monitoring standards.
Technical Leadership & Collaboration
Partner closely with Data Scientists, Product Managers, and Engineering teams to deliver production-ready AI solutions.
Provide technical guidance on architecture decisions, implementation strategies, and engineering best practices.
Mentor team members and contribute to building strong engineering capabilities across the organization.
Lead technical discussions, design reviews, and architecture assessments to ensure scalable and maintainable solutions.
Communicate technical concepts, trade-offs, and recommendations effectively to both technical and non-technical stakeholders.
Required Qualifications
Bachelor's degree in Computer Science, Engineering, Information Technology, or a related discipline.
Strong experience designing, implementing, and scaling production Machine Learning and Generative AI systems.
Hands-on expertise building LLM-powered applications, including RAG architectures, prompt orchestration, and AI workflow automation.
Experience designing and implementing agentic AI systems utilizing modern orchestration frameworks and multi-step workflow patterns.
Deep understanding of semantic search, embeddings, vector-based retrieval, and enterprise search platforms.
Strong experience with cloud-native application development and distributed systems architectures.
Hands-on experience with event-driven architectures, messaging systems, caching technologies, and large-scale data processing.
Advanced programming skills in Python and familiarity with modern systems programming languages.
Experience building and maintaining scalable API services and microservice-based applications.
Knowledge of containerization, orchestration platforms, and modern software deployment practices.
Strong software engineering foundations, including system design, testing methodologies, automation, and CI/CD principles.
Preferred Qualifications
Experience working with leading Generative AI and LLM ecosystems.
Knowledge of Infrastructure as Code (IaC), DevOps practices, and cloud automation tools.
Experience with big data technologies and distributed computation frameworks.
Familiarity with graph databases and knowledge graph technologies.
Experience designing highly available, fault-tolerant, and low-latency distributed systems.
Exposure to regulated, compliance-driven, or domain-intensive business environments.
Key Competencies
Strong architectural thinking with a focus on scalability, resilience, and maintainability.
Ownership mindset with accountability for delivery, quality, and operational excellence.
Ability to bridge the gap between data science innovation and production engineering.
Excellent collaboration skills across engineering, product, and analytics teams.
Strong troubleshooting, performance optimization, and problem-solving capabilities.
Ability to clearly articulate technical designs, risks, and implementation trade-offs.
Passion for innovation, continuous learning, and applying emerging AI technologies to solve business challenges

Senior Machine Learning Engineer

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