AWS Senior Cloud & ML Engineering Associate Director

Job description

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for an AWS Senior Cloud & ML Engineering Associate Director based in the United States.

Overview

This leadership opportunity is ideal for an experienced cloud and machine learning engineering professional ready to drive large-scale technology transformation. You will lead complex AWS engagements, designing secure, scalable, and production-ready cloud architectures that bridge data science innovation and real-world machine learning applications. Working closely with clients, data scientists, platform engineers, and technical leaders, you will turn business objectives into practical implementation strategies. You will champion cloud security, automation, resilience, and operational excellence while developing reusable engineering standards and solutions. Beyond technical delivery, you will mentor engineering teams, strengthen strategic client relationships, and contribute to business growth. Join a collaborative, client-focused environment where emerging AI technologies and cloud innovation create opportunities for meaningful impact.

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Accountabilities

• Lead AWS cloud architecture and implementation: Own the architecture, technical direction, and delivery outcomes of complex cloud engineering engagements, translating business requirements into actionable implementation plans.
• Drive machine learning and AI integration: Bridge data science research and production-grade AWS deployments, enabling faster experimentation, reliable ML infrastructure, and scalable AI solutions.
• Design secure and resilient cloud environments: Make architecture decisions covering networking, identity and access management, security, automation, observability, resilience, compliance, and AWS Well-Architected principles.
• Oversee technical delivery and risk management: Manage critical dependencies, identify and mitigate technical risks, resolve complex engineering challenges, and ensure high-quality project outcomes.
• Develop cloud engineering standards: Create reusable reference architectures, implementation patterns, technical frameworks, and delivery standards to improve consistency, efficiency, and maintainability.
• Lead and mentor technical teams: Guide cloud engineers and cross-functional specialists, review architecture decisions and critical deliverables, and support professional development through coaching and knowledge sharing.
• Strengthen client relationships: Provide strategic technical guidance to client technology leaders, build trusted partnerships, and identify opportunities to improve cloud capabilities and address evolving business needs.
• Support business development: Contribute to client proposals, solution design, engagement scoping, and growth strategies while identifying relevant opportunities for additional services.
• Ensure operational readiness: Facilitate sustainable transitions to ongoing operations and continuously improve architecture, security, reliability, automation, and operating models.

Requirements

• Education: Bachelor's degree in Management Information Systems, Information Systems, Computer Science, or a related discipline.
• Professional experience: At least seven years of experience in professional services or industry, including a minimum of five years of hands-on AWS experience across multiple service domains.
• Cloud architecture expertise: Strong experience designing and implementing AWS solutions across networking, identity, security, automation, resilience, observability, and cloud governance.
• Python development: Strong Python programming skills, with demonstrated ability to produce maintainable, reliable, production-quality code.
• Machine learning lifecycle: Familiarity with ML lifecycle and deployment tools such as Amazon SageMaker, MLflow, or equivalent technologies.
• Agentic AI experience: Experience with agentic AI frameworks and managed agent runtimes, including Amazon Bedrock Agents, AgentCore, or comparable solutions.
• Cross-functional collaboration: Experience working alongside data science and platform engineering teams to move technical concepts and research into production-ready solutions.
• Required certifications: AWS Certified Solutions Architect – Associate and AWS Certified Generative AI Developer.
• Preferred certifications: AWS Certified Solutions Architect – Professional, AWS Certified Machine Learning – Specialty, or AWS Certified DevOps Engineer.
• Technical leadership: Demonstrated ability to guide engineering teams, review complex technical deliverables, make architecture decisions, and resolve challenging delivery issues.
• Communication and stakeholder management: Strong interpersonal and communication skills, with the ability to explain complex technical concepts, influence senior stakeholders, and build lasting client relationships.
• Project and risk management: Ability to manage competing priorities, oversee technical dependencies, anticipate delivery risks, and maintain high standards of quality and operational readiness.
• Professional mindset: A proactive, analytical, and collaborative approach, with a commitment to continuous learning, mentoring, innovation, and business development.
• Location and mobility: Willingness to work in a hybrid environment and travel to client sites when required by project needs.

Benefits

• Competitive base salary: $153,000–$260,000 per year, depending on experience, skills, and geographic location.
• Performance-based incentives: Target bonus opportunity of 16%, with estimated total cash compensation ranging from $177,480 to $301,600 annually. Incentive compensation is subject to eligibility and applicable plan terms.
• Comprehensive healthcare: Medical, dental, and vision insurance, along with flexible spending accounts (FSA), health savings accounts (HSA), or other eligible healthcare spending arrangements.
• Financial protection: Life and accidental death and dismemberment insurance, short- and long-term disability coverage, and other eligible insurance benefits.
• Retirement savings: A 401(k) savings and investment plan with an employer match of 50% on the first 6% of employee contributions.
• Paid time off: Choice Time Off for vacation, personal needs, and sick time, with new hires receiving up to 20 days per calendar year, depending on applicable terms and eligibility.
• Paid holidays: Up to 11 recognized paid holidays each calendar year.
• Family support: Paid parental leave of up to 10 weeks, as well as adoption and fertility assistance.
• Professional growth: Opportunities to mentor technical teams, expand expertise in cloud and AI engineering, develop reusable solutions, and contribute to strategic client engagements.
• Collaborative culture: An environment focused on innovation, inclusion, teamwork, continuous learning, and meaningful client impact.

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How Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

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