Architect / Principal, AI ML Engineer

This position is posted by Jobgether on behalf of a partner company. We are currently looking for an Architect / Principal, AI ML Engineer in United States.

This role sits at the forefront of enterprise AI innovation, focusing on the design and architecture of advanced AI, ML, and Generative AI systems that power real business transformation.
You will define end-to-end solutions spanning data pipelines, model training, deployment, and scalable MLOps frameworks across cloud-native environments.
A key aspect of the role involves acting as a strategic advisor, guiding clients through solution design discussions and shaping AI adoption strategies.
You will work across cutting-edge areas such as LLM integration, RAG systems, Agentic AI, and MCP-based architectures.
The position blends deep technical leadership with client-facing consulting and cross-functional collaboration at a senior level.
You will also play a key role in mentoring engineering teams and shaping organizational AI capabilities.
This is a high-impact role for someone who thrives at the intersection of architecture, innovation, and business value creation.

• Define and lead end-to-end architecture for AI/ML, Generative AI, Agentic AI, and MCP-based systems, including data pipelines, model training/inference workflows, and scalable MLOps platforms.
• Act as a strategic advisor in client engagements, leading architecture discussions, presenting AI/ML solutions, and translating business problems into technical AI strategies.
• Design and implement scalable cloud-native AI solutions using platforms such as Azure ML, AWS SageMaker, or GCP Vertex AI.
• Lead the integration of Generative AI capabilities into enterprise applications using LLM APIs (OpenAI, Gemini, Claude) and open-source models such as LLaMA.
• Architect Retrieval-Augmented Generation (RAG) systems leveraging vector databases such as Pinecone, Weaviate, and FAISS.
• Guide MLOps practices, including CI/CD for ML, model versioning, monitoring, governance, and automated retraining pipelines.
• Evaluate emerging AI technologies, conduct build-vs-buy analysis, and define adoption strategies aligned with business and technical objectives.
• Mentor engineering teams and solution architects while promoting best practices in AI system design, responsible AI, and governance.
• Collaborate with leadership and stakeholders to align AI/ML strategy with broader organizational goals and innovation roadmaps.
• Represent the organization at industry events, conferences, and workshops as a subject matter expert in AI/ML.

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Requirements:

• 15+ years of industry experience in AI, machine learning, or data science, ideally within IT services or enterprise environments, with a Master’s degree in Computer Science, Engineering, Mathematics, or related field (PhD preferred).
• Deep expertise in AI architecture patterns, including RAG systems, Agentic AI, MCP-based systems, and prompt orchestration.
• Strong hands-on experience with Python and ML frameworks such as PyTorch, TensorFlow, scikit-learn, and XGBoost.
• Proven experience working with Generative AI APIs (OpenAI, Claude, Gemini), including prompt engineering, embeddings, and fine-tuning techniques.
• Strong background in designing enterprise-scale ML systems with MLOps tools such as MLflow, Kubeflow, or SageMaker Pipelines.
• Familiarity with modern software engineering practices, including APIs, microservices, Docker, and Kubernetes.
• Experience in data governance, model risk management, compliance, and responsible AI principles.
• Strong leadership and communication skills, with the ability to influence technical and non-technical stakeholders.
• Demonstrated ability to drive innovation, define AI strategy, and guide complex technical transformations.
• Exposure to large-scale data systems and AI integration into production software products is highly desirable.

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Benefits:

• Flexible remote-first or hybrid work environment
• Global collaboration with diverse, cross-cultural engineering teams
• Strong focus on employee well-being, including mental health support and generous time off
• Opportunities for accelerated career growth through diverse projects and industries
• Access to cutting-edge AI/ML technologies and enterprise-scale innovation initiatives
• Inclusive and equal opportunity workplace culture grounded in collaboration and respect
• Continuous learning and participation in global tech communities and events