AWS Senior Cloud & ML Engineering Associate Director

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.