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

In this role, you will:

• Lead complex technology initiatives including those that are companywide with broad impact
• Act as a key participant in developing standards and companywide best practices for engineering complex and large-scale technology solutions for technology engineering disciplines
• Design, code, test, debug, and document for projects and programs
• Review and analyze complex, large-scale technology solutions for tactical and strategic business objectives, enterprise technological environment, and technical challenges that require in-depth evaluation of multiple factors, including intangibles or unprecedented technical factors
• Make decisions in developing standard and companywide best practices for engineering and technology solutions requiring understanding of industry best practices and new technologies, influencing and leading technology team to meet deliverables and drive new initiatives
• Collaborate and consult with key technical experts, senior technology team, and external industry groups to resolve complex technical issues and achieve goals
• Lead projects, teams, or serve as a peer mentor

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, API's and databases
• Design, develop and implement Gen AI applications using LLM's
• 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