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Agentic AI-Sr Architect role

Logiciel Solutions IncSanta Clara, CA🇺🇸United StatesPosted 15 Sept 2026

Why This Role Stands Out

As an Agentic AI-Sr Architect at Logiciel Solutions Inc., you'll drive innovation at the forefront of AI, shaping cutting-edge solutions with significant impact. This role is ideal for experienced architects passionate about building advanced AI systems and eager to contribute to a reputable company. Embrace this opportunity to advance your career and make your mark in Santa Clara.

Quick Overview

Seniority
Mid Senior
Work mode
On Site
Location
Santa Clara, CA, United States
Posted
11 hours ago
AWSMachine LearningNLPScrumAgileAzureDeep LearningGoogle CloudJiraLLMPyTorchPythonTensorFlowVault

Job Description

Job Title: Agentic AI-Sr Architect role

Location: Santa Clara, CA (Onsite Work) Looking Only Local Candidates

Duration: Full time Permanent role

 

·       Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, or related field.

·       Advanced coursework or certifications in machine learning, deep learning, or NLP.

·       Strong mathematical and statistical foundation.

 Experience Range

·       6–10 years of experience in AI/ML/Deep Learning model development

·       2-3 years Hands-on experience with LLMs, NLP, or speech/voice AI systems

·       3-5 years of experience deploying AI solutions in production environments

·       Primary (Must have skills)* - To be Screened by TA Team

·       5+ years of experience in Python, PyTorch, TensorFlow, or similar frameworks

·       3-5 years of experience designing, training, and fine-tuning large language models or AI models, speech/voice AI systems, Realtime voice pipeline

·       5 years of experience in cloud AI platforms (AWS Sagemaker, Azure ML, Google Cloud Platform AI)

·       2+ years of experience in Agentic AI frameworks such as LangChain, LangGraph, A2A, MCP, and Multi agent orchestration

·       2-3 years of experience Familiarity with model evaluation metrics, bias detection, and optimization

·       5 years of experience in  integrating AI models into applications via APIs or pipelines

·       2-3 years’ experience in Azure services — Azure AI Speech and Translator, Azure OpenAI, AI Search, plus Container Apps/AKS, API Management, Event Hubs, Key Vault, and Application Insights.

Job Description of Role* (RNR) - To be Evaluated by Technical Panel (Define it to give more clarity)

Key technical skills :

·       Design, develop, and deploy advanced AI models to meet business requirements

·       Collaborate with data engineers, LLM Ops, and software teams for end-to-end solutions

·       Evaluate model performance, fine-tune, and ensure scalability and reliability

·       Research emerging AI/ML technologies and propose innovative solutions

·       Mentor junior AI engineers and review code/models for best practices

·       Soft skills/other skills - To be Evaluated by Hiring Manager (To define how this will be evaluated)

Communication Skills:

·       Clearly explain AI concepts and model behavior to technical and non-technical stakeholders

·       Present complex model results in a concise and actionable manner

Interpersonal Skills:

·       Collaborate effectively with cross-functional teams

·       Provide constructive feedback and mentor junior team members

·       Problem-Solving and Analytical Thinking:

·       Identify bottlenecks in model performance and propose solutions

·       Troubleshoot deployment or integration challenges proactively

·       Analyze data patterns and model outputs to derive insights

·       Apply structured thinking to optimize AI workflows and pipelines

 

Task/ Work Updates:

·       Prior experience in working on Agile/Scrum projects with exposure to tools like Jira/Azure DevOps

·       Provides regular updates, proactive and due diligent to carry out responsibilities.

 Expected Outcome:

·       Secondary Skills to be planned Post Hiring - Training Plan

·       Advanced LLM techniques, prompt engineering, and fine-tuning strategies

·       AI ethics, fairness, and responsible AI deployment

·       Continuous learning on emerging AI frameworks and architectures

·       LLMOps - layered evals (WER, entity F1, COMET), CI regression gates, tracing, cost-per-minute telemetry, model routing, and drift detection.

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