AI Engineer - SME
- Hiring from
- United States
- Work type
- Hybrid
- Posted
628,199 remote jobs, straight from company career pages
100% free · New jobs every minute
Show job descriptionHide job description
Education: Bachelor’s degree required
Experience: Minimum of 10 years of specialized AI/ML engineering experience
Position Summary
We are seeking a Senior AI/ML Engineer to build and deploy scalable, production-ready artificial intelligence, machine learning, and generative AI capabilities. This role develops models, pipelines, and microservices that support LLM applications, agentic workflows, and advanced analytics.
The engineer will support an in-house custom application, integrating AI services, retrieval systems, databases, and application components across secure operational environments. The position requires hands-on engineering, technical mentorship, and collaboration across teams to improve model performance, reliability, security, and maintainability.
Key Responsibilities
-
Build scalable AI/ML and generative AI models, pipelines, and microservices for production use.
-
Develop training, evaluation, and inference pipelines in Python using PyTorch, TensorFlow, or scikit-learn.
-
Deploy and manage models using MLOps tools, CI/CD pipelines, containerization, and monitoring frameworks.
-
Implement embeddings, vector databases, LLM applications, agentic AI, and tool-calling workflows.
-
Develop multi-model and multimodal capabilities spanning reasoning, perception, memory, and tool execution.
-
Implement temporal, spatial, geographic, quantitative, and computer vision AI capabilities.
-
Develop named entity recognition, contextual entity extraction, and entity resolution workflows.
-
Build multi-step retrieval and structured output generation capabilities with detailed source citations and traceable analytical outputs.
-
Create and optimize data parsing, ingestion, normalization, and ETL pipelines for structured and unstructured data.
-
Integrate vector, graph, and relational databases while supporting data provenance, lineage, and attribute-based access control.
-
Optimize models and services for accuracy, latency, cost, and scalability across distributed environments.
-
Integrate user interfaces, backend services, middleware, databases, and AI components within the custom application.
-
Support deployment across air-gapped, hybrid, and on-premises environments, including locally hosted GPU infrastructure.
-
Maintain technical documentation and align development with AI governance, ethical requirements, and security standards.
-
Mentor engineers and establish consistent engineering practices across teams.
Required Qualifications
-
Minimum of 10 years of specialized AI/ML engineering experience.
-
Advanced Python skills and a strong understanding of algorithms, data structures, and distributed systems.
-
Hands-on experience deploying production generative AI and LLM systems, embeddings, and vector databases.
-
Proficiency with PyTorch, TensorFlow, and scikit-learn.
-
Experience with one or more cloud platforms, including AWS, Azure, or Google Cloud.
-
Practical experience with MLOps tools such as MLflow, Azure Machine Learning, or Vertex AI.
-
Background in natural language processing, computer vision, or reinforcement learning.
-
Experience with CI/CD, containerization, model deployment, and production monitoring.
-
Experience working in Agile engineering environments.
-
Ability to mentor engineers and coordinate technical standards across teams.
Technical Requirements
Advanced AI and Generative AI Engineering
-
Multi-model and multimodal orchestration for reasoning, perception, memory, and tool execution.
-
Temporal, spatial, geographic, quantitative, and computer vision AI implementation.
-
Named entity recognition, contextual entity extraction, and entity resolution.
-
Multi-step retrieval and structured output generation.
-
Complex data parsing and streamlined ingestion.
-
Detailed source citation and traceable analytical outputs.
Data Architecture and ETL
-
Vector, graph, and multiple database orchestration.
-
Automated ETL pipeline creation, scheduling, and optimization.
-
Attribute-based access control (ABAC) at the data layer.
-
Provenance and end-to-end data lineage tracking.
-
Schema-aligned ingestion and normalization of diverse structured and unstructured data.
Enterprise Infrastructure and Cross-Domain Solutions
-
Air-gapped, hybrid, and on-premises operational environments.
-
Container orchestration and distributed systems management using Kubernetes.
-
Locally hosted GPU servers and hyperconverged nodes.
-
Multitenant cross-domain solutions (CDS).
Application Development and Integration
-
User interface, backend, and middleware engineering using TypeScript, React, Python, and FastAPI.
-
Integration with LanceDB, Lance Graph, PostgreSQL, Temporal.io, Docker, and Kubernetes.
-
Integration of reasoning components, retrieval systems, databases, and AI and generative AI services.
-
Development and maintenance of an in-house custom application using modern programming languages and frameworks.
Preferred Qualifications
-
Experience supporting federal, defense, or highly regulated environments.
-
Knowledge of ethical AI, governance frameworks, and secure development practices.
-
Experience with large-scale optimization, GPU acceleration, or distributed training.
-
Experience building APIs, microservices, and automated machine learning workflows.
