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Virtual Tokenization Jobs in Virginia (NOW HIRING)

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Virtual Tokenization information

What is the difference between Virtual Tokenization vs Blockchain Developer?

AspectVirtual TokenizationBlockchain Developer
CredentialsKnowledge of digital assets, blockchain basics, and security protocolsProgramming skills, blockchain platforms, smart contract development
Work EnvironmentFinancial institutions, fintech companies, digital asset platformsTech firms, blockchain startups, financial services
Industry UsageTokenizing assets like real estate, art, or securitiesDeveloping blockchain solutions, smart contracts, and decentralized apps

Virtual Tokenization involves converting real-world assets into digital tokens for easier transfer and management, often focusing on asset representation and security. Blockchain Developers design and implement blockchain protocols, smart contracts, and decentralized applications. While both roles work within blockchain technology, Virtual Tokenization emphasizes asset digitization, whereas Blockchain Developers focus on building the underlying blockchain infrastructure.

How much do crypto jobs pay?

Crypto jobs, including roles like blockchain developers, analysts, and tokenization specialists, typically offer salaries ranging from $60,000 to over $150,000 annually depending on experience, location, and skill level. Entry-level positions may start lower, while senior roles with specialized skills in smart contracts or security can command higher compensation.

What jobs pay you in bitcoin?

Jobs that pay in bitcoin include freelance roles such as programming, graphic design, and writing, often found on platforms that facilitate cryptocurrency payments. Some companies and remote employers also offer salaries or bonuses in bitcoin, especially in the tech and blockchain sectors. Skills in cryptocurrency transactions and familiarity with digital wallets are useful for these positions.

Who hires blockchain developers?

Blockchain developers are hired by technology companies, financial institutions, startups, and organizations implementing blockchain solutions. Employers often seek skills in smart contract development, cryptography, and blockchain platforms like Ethereum or Hyperledger, and may require relevant certifications or experience with programming languages such as Solidity or Python.

What companies are doing tokenization?

Several companies are actively involved in tokenization, including financial technology firms like TokenEx, Tokeny Solutions, and BitGo, which provide digital asset security and tokenization platforms. Major financial institutions and blockchain companies also develop tokenization solutions for assets, payments, and data security, often integrating blockchain technology and requiring knowledge of cryptography and smart contracts.
What are the most commonly searched types of Tokenization jobs in Virginia? The most popular types of Tokenization jobs in Virginia are:
What are popular job titles related to Virtual Tokenization jobs in Virginia? For Virtual Tokenization jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Virtual Tokenization jobs in Virginia look for? The top searched job categories for Virtual Tokenization jobs in Virginia are:
What cities in Virginia are hiring for Virtual Tokenization jobs? Cities in Virginia with the most Virtual Tokenization job openings:
Virtual Tokenization jobs near you

Generative AI Engineer - McLean, Virginia - Onsite( Local only ) - Inperson interview

Nexylum Global LLC

Mclean, VA • On-site

$100K - $137K/yr

Other

Posted 5 days ago

New

Job description

Hello
This is Adnan from Nexylum Global Technologies. We are seeking an onsite Senior Generative AI Engineer with over 10 years of experience for a contract opportunity.

We are seeking a highly skilled Generative AI Engineer to design, develop, and deploy cutting-edge AI-powered applications using Large Language Models (LLMs) and modern AI frameworks. The ideal candidate should have hands-on experience building GenAI solutions, integrating AI models with enterprise applications, and developing scalable AI services using cloud platforms.

The candidate will work closely with data scientists, software engineers, and business stakeholders to create intelligent applications such as AI assistants, chatbots, document processing systems, recommendation engines, and workflow automation solutions.

Location: McLean, Virginia - Onsite( Local only ) - In-person interview
Minimum experience required: 10 years

<>Key Responsibilities
  • Design, develop, and deploy Generative AI applications using Large Language Models (LLMs).
  • Build AI-powered chatbots, virtual assistants, document Q&A, summarization, and content generation solutions.
  • Develop Retrieval-Augmented Generation (RAG) pipelines using vector databases.
  • Integrate OpenAI, Azure OpenAI, Anthropic Claude, Google Gemini, or open-source LLMs into enterprise applications.
  • Create AI agents using frameworks such as LangChain, LlamaIndex, CrewAI, or AutoGen.
  • Fine-tune and optimize LLMs for enterprise-specific use cases.
  • Develop REST APIs and microservices for AI applications.
  • Implement prompt engineering techniques to improve AI model performance.
  • Work with structured and unstructured data for knowledge retrieval and semantic search.
  • Deploy AI applications on cloud platforms such as Azure, AWS, or Google Cloud.
  • Optimize model latency, scalability, and cost efficiency.
  • Ensure responsible AI practices, including security, governance, and compliance.
  • Collaborate with cross-functional teams in Agile/Scrum environments.
  • Stay updated with the latest advancements in Generative AI technologies.
Mandatory Skills<>Generative AI
  • Strong experience with Large Language Models (LLMs)
  • OpenAI GPT Models
  • Azure OpenAI
  • Anthropic Claude
  • Google Gemini
  • Llama, Mistral, or other open-source LLMs
Frameworks
  • LangChain
  • LlamaIndex
  • Semantic Kernel
  • CrewAI
  • AutoGen
  • Hugging Face Transformers
Retrieval-Augmented Generation (RAG)
  • RAG architecture
  • Embedding models
  • Semantic Search
  • Hybrid Search
  • Context Management
Vector Databases
  • Pinecone
  • FAISS
  • ChromaDB
  • Weaviate
  • Milvus
  • Azure AI Search
Programming Languages
  • Python (Mandatory)
  • SQL
  • JavaScript (Preferred)
Machine Learning & AI
  • Prompt Engineering
  • Fine-tuning LLMs
  • NLP
  • Transformers
  • Embedding Models
  • Tokenization
  • Model Evaluation
Cloud Platforms
  • Microsoft Azure
  • Azure OpenAI
  • AWS Bedrock
  • Google Vertex AI
API Development
  • FastAPI
  • Flask
  • REST APIs
  • GraphQL (Preferred)
Databases
  • PostgreSQL
  • MongoDB
  • SQL Server
  • Redis
DevOps & MLOps
  • Docker
  • Kubernetes
  • Git
  • CI/CD
  • MLflow
  • Azure DevOps
  • Jenkins
AI Tools
  • Prompt Engineering
  • AI Agents
  • Function Calling
  • Tool Calling
  • Model Monitoring
  • AI Evaluation Frameworks
  • Guardrails
  • Responsible AI
Version Control
  • Git
  • GitHub
  • Azure DevOps
Methodologies
  • Agile
  • Scrum

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