Gen AI Architect

This range is provided by TEKFORTUNE INC. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

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Passionate about connecting talent with opportunity. Proven track record in strategic recruitment and seamless project delivery.

Note-Only USC, GC, or GC-EAD candidates are eligible to apply.

• 10+ years of experience in software engineering,
  machine learning, data science, or artificial intelligence
  .

Key Skill:
LLMs (Large Language Models), including fine-tuning
, LLMOps
, function calling
, and Retrieval-Augmented Generation (RAG),
Py Torch ,
Tensor Flow
, Transformers/Hugging Face
, and Num Py
.

• Sound experience with Retrieval-Augmented Generation (RAG), fine-tuning, and multi‑agent orchestration.
• Experienced in developing GenAI applications leveraging multi‑agent frameworks and/or graph‑based GenAI approaches (e.g.,
  GraphRAG
  ).
• Proficient in using common NLP and/or ML Python frameworks
  , such as Py Torch ,
  Tensor Flow
  , Transformers/Hugging Face
  , and Num Py
  .
• LLM skills including fine‑tuning,
  LLMOps, function‑calling, and retrieval augmented generation (RAG).
• Familiarity with data governance
  , AI ethics
  , and responsible AI practices
  .
• Strong proficiency in Python.
• Experience following software best practices in team settings, including version control (Git),
  CI/CD, documentation, & unit testing.
• Exposure to Microsoft Azure or similar cloud computing ecosystem.
• Ability to design scalable solutions and optimize performance for business impact.
• Strong problem‑solving skills and the ability to work in a fast‑paced, dynamic environment.
• Familiarity with vector databases, RAG pipelines, and agentic frameworks.
• Excellent communication and documentation skills.

• Advanced GenAI Expertise
  :
  Experience developing applications using multi‑agent frameworks and/or graph‑based approaches such as GraphRAG and Lang Graph
  .
• Cloud & MLOps Proficiency
  :
  Hands‑on experience with Azure AI services
  , containerization (Docker/Kubernetes), and ML pipelines
  .

• Design and develop GenAI‑based applications using advanced techniques such as Retrieval‑Augmented Generation (RAG),
  text‑to‑SQL, function calling, and agentic architectures.
• Implement multi‑agent frameworks and explore graph‑based GenAI approaches (e.g.,
  GraphRAG
  ) for complex problem‑solving.
• Define and enforce evaluation standards and best practices for GenAI agents, RAG pipelines, and multi‑agent orchestration.
• Performance evaluations to optimize ML and GenAI models for accuracy, scalability, and business impact.
• Engage with business stakeholders to understand requirements
  , gather feedback
  , and tailor solutions to meet strategic goals.
• Translate business needs into technical specifications and actionable plans.
• Ensure adherence to software engineering best practices
  , including version control (Git),
  CI/CD pipelines
  , documentation
  , and unit testing
  .
• Stay current with emerging GenAI evaluation tools, frameworks, and methodologies.
• Provide technical leadership and mentor team members on best practices and emerging GenAI technologies.

Mid‑Senior level

Contract

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