Sr Lead Software Engineer

Sr Lead Software Engineer (AI/ML, Conversational & Generative AI)

Lead the end-to-end engineering of next-generation, AI-powered software solutions—designing and delivering scalable conversational and generative AI systems at enterprise scale.

Responsibilities

• Lead end-to-end development: architecture → implementation → deployment → operational support for conversational & generative AI systems.
• Drive technical excellence across multiple initiatives, aligned with business goals and enterprise architecture standards.
• Own the AI solution lifecycle: design, development, testing, deployment, and ongoing optimization.
• Oversee complex AI projects to ensure timely delivery, technical accuracy, and alignment with product objectives.
• Collaborate with data scientists, engineers, and business stakeholders to turn requirements into scalable AI solutions.
• Integrate AI systems with enterprise platforms via APIs, databases, CRM systems, and Google Cloud services.
• Design and implement MLOps/AIOps: CI/CD pipelines, monitoring, versioning, and observability.
• Optimize AI models for performance, scalability, latency, reliability, and UX.
• Establish documentation standards, technical governance, and best practices.
• Mentor junior engineers and contribute to engineering culture and knowledge sharing.

Requirements

• 10+ years software engineering experience; 4+ years focused on AI/ML solution development and deployment.
• Strong Python proficiency.
• Experience with conversational AI platforms such as Dialogflow and Agent Builder.
• Deep experience with Google Cloud Platform (GCP), including Vertex AI and related AI/ML tools.
• Strong grounding in machine learning concepts, generative AI, and agentic system design.
• Backend API integration, system architecture, and enterprise application development experience.
• Demonstrated leadership: mentoring and cross-functional collaboration.
• Strong analytical/problem-solving and communication skills.
• Familiarity with MLOps/AIOps, observability, and production AI monitoring.

Nice-to-haves

• MLOps/AIOps with strong production monitoring and operational rigor for AI systems.
• Experience mentoring and establishing AI engineering governance/best practices.

Benefits / Work Model

• Fully remote within the United States.
• Competitive compensation based on experience and location.
• Comprehensive health, life, and wellness benefits.
• Career growth, mentorship, and professional development opportunities.