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As a Machine Learning Engineer, you will develop models for NLP and code-generation systems, analyze data, and improve AI performance using advanced algorithms. You'll manage the entire ML lifecycle and ensure effective communication of model benefits across the organization.

Develop and maintain scalable, reliable back-end systems and APIs, write maintainable code, and collaborate with global teams in a hybrid work environment.

Lead the development and deployment of advanced machine learning models focusing on NLP, while ensuring model integrity and communication with stakeholders.

Design and develop user interfaces and backend architectures for web applications, ensuring seamless integration and optimal performance using modern web technologies.

The Software Engineering Lead will design and maintain enterprise software applications, develop APIs, and build back-end systems, ensuring quality and best practices.

Design, build, and operate a multi-tenant, microservices-based platform on Kubernetes across cloud providers. Implement core platform components (Golang/Python), service mesh, observability, IaC, CI/CD, SLO-driven reliability, database and distributed-systems optimizations, incident response, and cost/performance tuning.

Lead end-to-end delivery and stabilization of agentic AI solutions from design through production. Translate business workflows into feasible AI workflows, provide technical oversight, coordinate stakeholders, de-risk integrations and data issues, report progress and outcomes, and mentor delivery teams to ensure reliable, scalable production deployments.

Owns a portfolio of strategic enterprise accounts end-to-end: accountable for delivery orchestration, post-go-live outcomes, executive stakeholder leadership, and commercial expansion. Staffs and directs delivery teams, ensures solution quality and ROI, runs QBRs, resolves escalations, translates customer needs into product feedback, and coaches teams to drive continuous improvement and scalable account practices.

Lead full-cycle technical recruiting for engineering and product teams: intake, sourcing, screening, interview coordination, offers, and onboarding. Build proactive pipelines, run intake meetings, partner with hiring managers on scorecards and interview loops, improve hiring processes and metrics, maintain ATS hygiene, and ensure an excellent candidate experience while driving hiring velocity.

Lead AI strategy for high-value enterprise accounts: advise C-level stakeholders, shape transformation roadmaps and business cases, influence deal strategy with executive leadership, structure engagements and proposals, run workshops and discovery sessions, and collaborate with product, engineering, GTM and delivery teams to align solutions and mentor internal teams on strategic storytelling and engagement design.