ZipRecruiter

Log In

2

Remote Kernel Developer Jobs in Washington, DC (NOW HIRING)

previous page

Showing results 21-25

All JobsKernel Developer JobsRemote Kernel Developer JobsRemote Kernel Developer Jobs in Washington, DC

Remote Kernel Developer information

See Washington, DC salary details

$49

$71

$106

How much do remote kernel developer jobs pay per hour?

As of Aug 6, 2026, the average hourly pay for remote kernel developer in Washington, DC is $72.00, according to ZipRecruiter salary data. Most workers in this role earn between $59.90 and $75.67 per hour, depending on experience, location, and employer.
What are the most commonly searched types of Kernel Developer jobs in Washington, DC? The most popular types of Kernel Developer jobs in Washington, DC are:
What are popular job titles related to Remote Kernel Developer jobs in Washington, DC? For Remote Kernel Developer jobs in Washington, DC, the most frequently searched job titles are:
What job categories do people searching Remote Kernel Developer jobs in Washington, DC look for? The top searched job categories for Remote Kernel Developer jobs in Washington, DC are:
Remote Kernel Developer jobs near you
Infographic showing various Remote Kernel Developer job openings in Washington, DC as of June 2026, with employment types broken down into 95% Full Time, 2% Part Time, and 3% Contract. Highlights an 48% Physical, 3% Hybrid, and 49% Remote job distribution, with an average salary of $149,754 per year, or $72 per hour.

Senior AI Solution Architect

Interon IT Solutions

Chantilly, VA • Remote

Contractor

Re-posted 25 days ago

Job description

#W2 Role
 
Role: Senior AI Solution Architect
Location: Remote
Experience: 10+ years preferred
Mandatory Skills: GCP, Vector Database, Agentic AI Frameworks, Python
 
Job Description
We are looking for a highly experienced and hands-on Senior AI Solution Architect to lead the design, development, and deployment of a scalable Agentic AI platform focused on knowledge-driven reasoning, enterprise transformation, and intelligent automation.
The ideal candidate should have strong experience in cloud modernization, AI architecture, enterprise API transformation, vector databases, Python development, and GCP-based AI/ML solutions. This role requires someone who can work closely with business, architecture, engineering, data, and cloud teams to transform enterprise services into AI-driven, searchable, and reusable capabilities through Knowledge Record Management, reasoning layers, orchestration frameworks, and agentic workflows.
 
Key Responsibilities
Lead the architecture, design, and implementation of a scalable Agentic AI platform for enterprise use cases.
Design AI-driven solutions focused on knowledge discovery, reasoning, orchestration, and enterprise automation.
Build and guide the development of reasoning and orchestration layers using Agentic AI frameworks.
Transform enterprise services, documents, and business knowledge into searchable APIs and reusable AI capabilities.
Design and implement Knowledge Record Management patterns to structure, classify, retrieve, and govern enterprise knowledge.
Build RAG-based and agentic workflows using vector databases, embeddings, retrieval pipelines, and LLM orchestration.
Develop AI solution architecture on Google Cloud Platform (GCP) using cloud-native services and scalable deployment models.
Lead cloud modernization and migration strategies to enable AI-first enterprise platforms.
Design secure, scalable, and reusable APIs to expose enterprise knowledge and services to AI agents.
Provide hands-on technical leadership in Python-based AI engineering, orchestration, and backend service development.
Evaluate and recommend AI frameworks, vector database platforms, embedding models, and orchestration patterns.
Partner with product owners, business stakeholders, architects, and engineering teams to convert business needs into AI solution designs.
Create architecture diagrams, technical design documents, API specifications, integration patterns, and implementation roadmaps.
Define best practices for responsible AI, security, access control, data privacy, observability, and governance.
Support proof-of-concepts, MVPs, production deployments, performance tuning, and platform scaling.
Mentor engineering teams on Agentic AI architecture, cloud-native design, and enterprise AI delivery practices.
 
Required Skills
10+ years of overall IT experience with strong experience in solution architecture, cloud architecture, AI/ML engineering, or enterprise application modernization.
Strong hands-on experience with Google Cloud Platform (GCP).
Strong experience designing and implementing Agentic AI frameworks and multi-agent workflows.
Hands-on experience with Python for AI engineering, backend services, orchestration, and API development.
Strong experience with vector databases such as Pinecone, Weaviate, Milvus, Chroma, FAISS, Vertex AI Vector Search, or similar platforms.
Experience building embeddings, semantic search, RAG pipelines, reasoning workflows, and knowledge retrieval systems.
Strong understanding of LLM-based solution design, prompt engineering, tool calling, function calling, and agent orchestration.
Experience building AI-driven APIs and transforming enterprise services into searchable, consumable, and reusable AI capabilities.
Strong understanding of cloud modernization, migration patterns, microservices, APIs, and enterprise integration.
Experience designing scalable, secure, and production-ready AI platforms.
Strong knowledge of data pipelines, metadata, document ingestion, chunking strategies, indexing, retrieval optimization, and knowledge governance.
Ability to define architecture standards, reusable design patterns, and technical implementation roadmaps.
Strong stakeholder management, communication, documentation, and leadership skills.
 
Preferred Skills
Experience with GCP services such as Vertex AI, BigQuery, Cloud Run, Cloud Functions, Cloud Storage, Pub/Sub, Cloud SQL, GKE, IAM, and API Gateway.
Experience with Agentic AI frameworks such as LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar tools.
Experience with Knowledge Graphs, ontology design, metadata management, taxonomy design, or enterprise knowledge management.
Experience designing reasoning/orchestration layers for enterprise GenAI platforms.
Experience with API management, REST APIs, GraphQL, event-driven architecture, and microservices.
Experience with MLOps, CI/CD, Docker, Kubernetes, Terraform, and cloud deployment automation.
Knowledge of security, privacy, governance, and compliance requirements for enterprise AI platforms.
 
Nice to Have
Experience with cloud migration and modernization from legacy platforms to GCP.
Experience implementing AI platforms for enterprise search, knowledge assistants, service automation, or intelligent workflow orchestration.
Exposure to model evaluation, LLM observability, hallucination controls, guardrails, and prompt/version management.
Experience working in financial services, healthcare, insurance, retail, or large enterprise environments.
 
Certifications
GCP Professional Cloud Architect, Professional Machine Learning Engineer, or related cloud/AI certifications are a plus.