Senior Software Engineer - GEN AI /Python / LLM / ML Ops/ RPA

About this role:

• Wells Fargo is seeking a Senior Software Engineer.

In this role, you will:

• Lead moderately complex initiatives and deliverables within technical domain environments
• Contribute to large scale planning of strategies
• Design, code, test, debug, and document for projects and programs associated with technology domain, including upgrades and deployments
• Review moderately complex technical challenges that require an in-depth evaluation of technologies and procedures
• Resolve moderately complex issues and lead a team to meet existing client needs or potential new clients needs while leveraging solid understanding of the function, policies, procedures, or compliance requirements
• Collaborate and consult with peers, colleagues, and mid-level managers to resolve technical challenges and achieve goals
• Lead projects and act as an escalation point, provide guidance and direction to less experienced staff

Required Qualifications:

• 4+ years of Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education

Desired Qualifications:

• Strong Python programming skills
• Expertise in RPA tools such as UI path
• Expertise in Workflow automation tools such as Power Platform
• Minimum 2 Years of hands-on experience in AI/ML and Gen AI
• Proven experience in LLMs (Gemini, GPT or Llama etc.)
• Extensive experience in Prompt Engineering and model fine tuning
• AI/Gen AI Certifications from premier institution
• Hands on experience in ML ops (MLflow, CICD pipelines)

Job Expectations:

• Design and develop AI driven Automation solutions
• Implement AI automation to enhance process automation
• Develop and maintain automation, BOTS and AI based workflows
• Integrate Ai automation with existing applications, APIs and databases
• Design, develop and implement Gen AI applications using LLMs
• Build and optimize prompt engineering workflows
• Fine tune and integrate pre-trained models for specific use cases
• Deploy models in production using robust MLops practices