Senior Delivery Consultant - AI & Data, Professional Services
Amazon is hiring a Senior Delivery Consultant - AI & Data, Professional Services. Level rates it ; you can apply on Level.
AI in this role
Possessing a deep understanding of AWS products and services, as a Delivery Consultant you will be proficient in architecting complex, scalable, and secure solutions tailored to meet the specific needs of each customer. You’ll work closely with stakeholders to gather requirements, assess current infrastructure, and propose effective migration strategies to AWS. As trusted advisors to our customers, providing guidance on industry trends, emerging technologies, and innovative solutions, you will be responsible for leading the implementation process, ensuring adherence to best practices, optimizing performance, and managing risks throughout the project.
The AWS Professional Services organization is a global team of experts that help customers realize their desired business outcomes when using the AWS Cloud. We work together with customer teams and the AWS Partner Network (APN) to execute enterprise cloud computing initiatives. Our team provides assistance through a collection of offerings which help customers achieve specific outcomes related to enterprise cloud adoption. We also deliver focused guidance through our global specialty practices, which cover a variety of solutions, technologies, and industries.
Key job responsibilities
As an experienced technology professional, you will be responsible for:
1. Designing and implementing complex, scalable, and secure AWS solutions tailored to customer needs
2. Providing technical guidance and troubleshooting support throughout project delivery
3. Collaborating with stakeholders to gather requirements and propose effective migration strategies
4. Acting as a trusted advisor to customers on industry trends and emerging technologies
5. Sharing knowledge within the organization through mentoring, training, and creating reusable artifacts
About the team
Diverse Experiences: AWS values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job below, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture - Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness.
Mentorship & Career Growth - We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
Work/Life Balance - We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.
Basic qualifications
- 5+ years of experience designing and implementing data lakes, data warehouses, or lakehouse solutions, including hands-on experience with open table formats (e.g., Apache Iceberg, Delta Lake, Apache Hudi)
- Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field, or equivalent practical experience
- Proficiency in SQL and at least two programming languages (e.g., Python, Java, Scala), with experience building ETL/ELT pipelines and managing large-scale data
- Hands-on experience with distributed data processing frameworks such as Apache Spark (including Spark SQL), Hive, or similar technologies
- Experience with MPP data warehouse technologies (e.g., Amazon Redshift, Teradata, Snowflake) and data modeling
- Experience with streaming and messaging platforms such as Apache Kafka, Amazon Kinesis, or Amazon MSK
- Hands-on experience building generative AI solutions, including LLMs, Retrieval-Augmented Generation (RAG), and vector databases, with experience taking solutions to production
- Experience with infrastructure-as-code and workflow automation tools (e.g., Terraform, AWS CDK, CloudFormation, Airflow, CI/CD pipelines)
- Experience in a customer-facing role, with excellent written and verbal communication skills and the ability to present technical solutions to both technical and executive audiences
Preferred qualifications
- Experience with AWS data and AI services such as Amazon S3, AWS Glue, Amazon EMR, Amazon Athena, AWS Lake Formation, Amazon Redshift, Amazon Kinesis, Amazon Bedrock, Amazon SageMaker, and Amazon OpenSearch
- Experience with the Hadoop ecosystem (e.g., HDFS, Hive, YARN) and migrating on-premises Hadoop or legacy data warehouse workloads to AWS (e.g., Amazon EMR, S3, Glue, Athena)
- Experience with agentic architectures and frameworks (e.g., Strands Agents, LangChain, LangGraph) and with the Model Context Protocol (MCP)
- Experience with LLM evaluation, fine-tuning or customization, guardrails, and LLMOps/MLOps
- PhD or Master's degree in Computer Science, Engineering, Mathematics, or related field
Amazon est un employeur engagé pour l'égalité des chances. Nous sommes convaincus qu'une main d'oeuvre diversifée est essentielle à notre réussite. Nous prenons nos décisions de recrutement en fonction de votre expérience et de vos compétences. Nous apprécions votre envie de découvrir, d'inventer, de simplifier et de construire. La protection de votre vie privée et la sécurité de vos données constituent depuis longtemps une priorité absolue pour Amazon. Veuillez consulter notre Politique de Confidentialité pour en savoir plus sur la façon dont nous collectons, utilisons et traitons les données personnelles de nos candidats.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
How we rate this
Senior Delivery Consultant - AI & Data, Professional Services at Amazon rates 64 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.
Works on AI. The daily work is on AI products, without building the model.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Also common in AI consultant jobs at this level
Pay
- Typical AI consultant job
- $152k
This posting doesn't list pay. Use the typical AI consultant figure when pay comes up. See AI consultant jobs
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- How do you monitor a model once it's live, and how do you know it needs retraining?
- How do you decide that one model's output is better than another's for a given task?
- Walk me through how you've used LangChain in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: RAG, Fine-tuning, ML Ops, AI Evaluation, and LangChain. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.
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