What are the responsibilities and job description for the Generative AI Architect position at Saransh Inc?
Job Title: Generative AI Architect
Location: Charlotte, NC (Onsite from Day 1)
Job Type: Contract
Skill Metrics
Claude
Microsoft GitHub Copilot
PyTorch
What are the top skills required for this role?
Location: Charlotte, NC (Onsite from Day 1)
Job Type: Contract
Skill Metrics
Claude
Microsoft GitHub Copilot
PyTorch
What are the top skills required for this role?
- Hands on experience of driving gen AI solution using Copilot, Claude Code etc in the STLC
- Participate in design and implementation of solutions, fine-tuning pipelines, and prompt engineering frameworks
- Participate in cross-functional GenAI initiatives and PoC’s across entire software lifecycle
- Seeking a visionary and experienced Senior Generative AI Architect to lead the design, development, and implementation of generative AI solutions across software test lifecycle.
- The selected candidate will define the strategic roadmap for GenAI, evaluate and select appropriate architectures, and ensure the scalability, security, and performance of deployed models.
- This is a highly collaborative role that bridges client needs, R&D innovation, and enterprise-grade AI engineering.
- GenAI solution in STLC
- Build/Test Solution to create test automation code for UI/API through GenAI assisted coding
- Build/Test solution for test data mining using Generative AI
- Architecture & Design
- Participate in design and implementation of solutions, fine-tuning pipelines, and prompt engineering frameworks
- Participate in cross-functional GenAI initiatives and PoC’s across entire software lifecycle
- Strategy & Roadmap
- Define GenAI adoption roadmap in alignment with client goals
- Evaluate build-vs-buy decisions and advise on vendor/platform selections
- Advocate for responsible AI practices and model governance frameworks
- Collaboration & Leadership
- Collaborate with data scientists, ML engineers, product managers, and key stakeholders
- Mentor teams in GenAI best practices, including model optimization, deployment, and safety measures
- Innovation & Experimentation
- Stay ahead of the curve on advancements in transformer architectures, LLM training, and multimodal AI.
- Prototype custom models using frameworks like LangChain, PyTorch, etc.