GEN AI Architect

Overview

Remote
Depends on Experience
Contract - W2
Contract - Independent
Contract - 6 Month(s)

Skills

Vertex AI
RAG
Agentic AI
LLMs(Open AI or Gemini)

Job Details

Job Title: Gen AI Architect

Type: Contract(C2C)

Duration: 6 Months +

Location: Complete Remote

  • Architect large complex Agentic AI systems and implement end-to-end integrating ML components, training pipelines, inference services, tools, evaluations, and safety.
  • Experience in agentic architecture-based implementations and advanced Retrieval Augmented Generation.
  • Utilize Generative AI models and frameworks such as OpenAI family, Gemini, open source LLMs, Dall-e, LlamaIndex, Langchain, and Retrieval Augmented Generation (RAG).
  • Design and implement production-grade Machine Learning models using Google Vertex AI and Python for Conversational AI.
  • Fine-tune and deploy large foundational models including LLMs, LVMs, and LMMs for various real-world mapping tasks.
  • Contribute to and scale AI infrastructure to facilitate quick experimentation and iteration.
  • Collaborate with cross-functional teams to evolve early-stage ideas into robust, production-ready systems.
  • Implement AI data pipelines to merge structured, semi-structured, and unstructured data for AI and Agentic solutions.
  • Develop data domains and products for different consumption archetypes like Reporting, Data Science, AI/ML, and Analytics.
  • Ensure reliability, availability, and scalability of data pipelines through effective monitoring and incident management.
  • Implement best practices in reliability engineering and collaborate with DevOps teams for seamless deployment and operation of data systems.
  • Conduct thorough testing and validation of models and optimize them for performance and accuracy.
  • Experience with containerization(GKE) and scaling models, integrating models with downstream systems.
  • Knowledge of Data Architecture Design and Modelling, familiarity with Cloud Computing environments.
  • Strong software engineering fundamentals in developing production systems with clean, robust, reliable, and maintainable code.
  • Hands-on experience with processing large datasets, training ML models in distributed environments.
  • Proficiency in Python, TensorFlow, containerization, FastAPI, REST/GraphQL.
  • Strong interpersonal collaboration and communication skills, ability to work with high ambiguity and minimum supervision.

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