Senior MLAI Engineer
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Job Location:
Charlotte, VT - USA
Monthly Salary:
Not Disclosed
Posted on:
8 hours ago
Vacancies:
1 Vacancy
Job Summary
Job Description:-
Job description
Senior ML/AI Engineer
This role is critical for designing developing and optimizing the core AI and RAG components of the system.
Senior ML/AI Engineer
This role is critical for designing developing and optimizing the core AI and RAG components of the system.
Gen AI: Deep understanding of generative AI models LLMs and their application in conversational interfaces. Experience with fine-tuning or adapting models for specific tasks.
Lang Graph: Expertise in building agentic systems and state machines using Lang Graph. Understanding of agent orchestration managing conversational flow and integrating various tools/models.
Python: Strong proficiency in Python including experience with relevant AI/ML libraries (e.g. TensorFlow PyTorch scikit-learn Hugging Face).
RAG Implementation: Experience in designing and implementing Retrieval Augmented Generation systems including vector databases embedding techniques and efficient retrieval strategies.
Cloud Platform (Google Cloud): Experience deploying and managing AI/ML workloads on Google Cloud Platform (GCP) has added advantage. Familiarity with relevant services like Vertex AI Cloud Functions Cloud Run or GKE for model deployment.
Async Programming: Have understanding or ability to write efficient asynchronous code for handling potentially long-running AI model calls and external API interactions.
REST: Understanding of RESTful principles for integrating AI services with other parts of the system.
Logging: Experience with implementing robust logging for monitoring AI model performance identifying issues and tracking usage. Familiarity with tools like Splunk is a plus.
Lang Graph: Expertise in building agentic systems and state machines using Lang Graph. Understanding of agent orchestration managing conversational flow and integrating various tools/models.
Python: Strong proficiency in Python including experience with relevant AI/ML libraries (e.g. TensorFlow PyTorch scikit-learn Hugging Face).
RAG Implementation: Experience in designing and implementing Retrieval Augmented Generation systems including vector databases embedding techniques and efficient retrieval strategies.
Cloud Platform (Google Cloud): Experience deploying and managing AI/ML workloads on Google Cloud Platform (GCP) has added advantage. Familiarity with relevant services like Vertex AI Cloud Functions Cloud Run or GKE for model deployment.
Async Programming: Have understanding or ability to write efficient asynchronous code for handling potentially long-running AI model calls and external API interactions.
REST: Understanding of RESTful principles for integrating AI services with other parts of the system.
Logging: Experience with implementing robust logging for monitoring AI model performance identifying issues and tracking usage. Familiarity with tools like Splunk is a plus.
Key Skills
- APIs
- C/C++
- Computer Graphics
- Go
- React
- Redux
- Node.js
- AWS
- Library Services
- Assembly
- GraphQL
- High Voltage
