AI Software Engineer

About The Company Seeq is a leading provider of advanced analytics software tailored for the process manufacturing industries, including pharmaceuticals, mining, renewables, and energy sectors. The company’s platform enables organizations to harness massive volumes of time-series and event data generated from industrial operations, transforming raw data into actionable insights that drive operational excellence, profitability, and sustainability. Founded by experienced entrepreneurs, Seeq operates as a fully remote organization, leveraging modern collaboration tools such as Zoom, Slack, and its proprietary Qube virtual office to foster seamless teamwork across diverse geographies. Recognized for its innovative solutions and vibrant company culture, Seeq has been featured in the Technology Fast 500 and Inc. Magazine’s Best Places to Work.

About The Role The AI Software Engineer at Seeq is a senior-level individual contributor responsible for defining and scaling the backend platforms and AI systems that support Seeq’s intelligent applications. This role involves owning complex, ambiguous projects that span multiple teams and require architectural leadership to establish shared infrastructure, services, APIs, and engineering patterns. The ideal candidate will bring extensive experience in building AI systems, coupled with strong expertise in backend and distributed systems, to help establish reliable, scalable, and safe AI-enabled capabilities across the organization. This is a hands-on role that demands a deep technical skill set, including designing, coding, debugging, and deploying production software, while also providing mentorship and technical guidance to engineering teams.

Qualifications

• Minimum 10+ years of professional software engineering experience, including operating at Staff or equivalent scope
• Significant recent experience in generative AI or agentic systems
• Proven track record leading complex AI, agentic, or platform initiatives from concept to production
• Hands-on experience developing and operating agentic systems, including routing, orchestration, tool use, evaluations, and runtime infrastructure
• Deep expertise in Python and backend engineering, including designing large-scale distributed systems, APIs, and data workflows
• Experience designing or managing shared platform services used by multiple engineering teams
• Proficiency with modern AI/LLM frameworks, SDKs, and orchestration tools for production AI applications
• Experience developing evaluation strategies and observability for AI systems
• Familiarity with SQL and relational databases such as PostgreSQL
• Experience deploying and managing production systems in Kubernetes or similar containerized environments
• Background in SaaS environment deployment and operation
• Strong understanding of AI reliability, performance, monitoring, and cost management
• Ability to independently evaluate emerging AI approaches and translate them into maintainable solutions
• Excellent communication skills and proven mentorship abilities
• Demonstrated ownership of ambiguous technical problems and leadership in execution

Responsibilities

• Define and set the technical direction for backend and AI platform capabilities, including architecture, patterns, and approaches for scalable systems
• Lead high-impact AI and platform initiatives, managing projects from conception through deployment while coordinating across teams and stakeholders
• Design and develop agentic systems supporting routing, orchestration, tool integration, evaluation, and agent communication
• Build and evolve shared platform infrastructure, including backend services, runtime environments, and APIs that enable other teams to develop AI-enabled applications
• Develop reliable distributed systems and data workflows capable of handling large-scale AI workloads
• Create reusable AI platform components, such as APIs, libraries, orchestration patterns, and developer tools
• Establish evaluation and monitoring strategies to ensure the safety, reliability, and observability of AI systems
• Build integrations facilitating communication between AI agents, internal services, and third-party platforms
• Translate emerging AI technologies into pragmatic, production-ready systems, driving work from idea to deployment
• Engage directly with customers during early deployments to gather insights and refine AI capabilities based on real-world performance
• Mentor and …