Are you excited about building software solutions around large, complex Machine Learning (ML) and Artificial Intelligence (AI) systems? Want to help the largest global enterprises derive business value through the adoption and automation of Generative AI (Gen. AI)? Excited by using massive amounts of disparate data to develop AI/ ML models? Eager to learn to apply AI/ ML to a diverse array of enterprise use? Thrilled to be a key part of Amazon, who has been investing in Machine Learning for decades - pioneering and shaping the worlds AI technology? The Amazon Web Services Professional Services (Pro. Serve) team is seeking a skilled ML Engineer to join our team as a Delivery Consultant at Amazon Web Services (AWS). In this role, you'll work closely with customers to design, implement, and manage AWS AI/ ML and Gen. AI solutions that meet their technical requirements and business objectives. You'll be a key player in driving customer success through their cloud journey, providing technical expertise and best practices throughout the ML project lifecycle. Possessing a deep understanding of AWS products and services, as a Delivery Consultant you will be proficient in architecting complex, scalable, and secure AI/ ML and Gen. AI solutions tailored to meet the specific needs of each customer. Youll 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. Key job responsibilities. As an experienced technology professional, you will be responsible for:1. Implementing end-to-end AI/ ML and Gen. AI projects, from understanding business needs to data preparation, model development, deployment and monitoring 2. Designing and implementing machine learning pipelines that support high-performance, reliable, scalable, and secure ML workloads 3. Designing scalable ML solutions and operations (ML - Ops) using AWS services and leveraging Gen. AI solutions when applicable 4. Collaborating with cross-functional teams (Applied Science, DevOps, Data Engineering, Cloud Infrastructure, Applications) to prepare, analyze, and operationalize data and AI/ ML models 5. Serving as a trusted advisor to customers on AI/ ML and Gen. AI solutions and cloud architectures 6. Sharing knowledge and best practices within the organization through mentoring, training, publication, and creating reusable artifacts 7. Ensuring solutions meet industry standards and supporting customers in advancing their AI/ ML, Gen. AI, and cloud adoption strategies. This is a customer-facing role with potential travel to customer sites as needed. About the team. Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasnt followed a traditional path, or includes alternative experiences, dont let it stop you from applying. Why AWS Amazon Web Services (AWS) is the worlds most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating thats why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. 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, theres nothing we cant achieve in the cloud. Inclusive Team Culture AWS values curiosity and connection. Our employee-led and company-sponsored affinity groups promote inclusion and empower our people to take pride in what makes us unique. Our inclusion events foster stronger, more collaborative teams. Our continual innovation is fueled by the bold ideas, fresh perspectives, and passionate voices our teams bring to everything we do. Mentorship and Career Growth. Were continuously raising our performance bar as we strive to become Earths Best Employer. Thats why youll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Basic Qualifications- Bachelor's degree in Computer Science, Engineering, a related field, or equivalent experience- 3 years of cloud architecture and solution implementation experience- 3 years data, software, or ML engineering, with understanding of distributed computing (e.g., data pipelines, training and inference, ML infrastructure design)- 3 years developing predictive modeling, natural language processing, and deep learning, with experience in building and deploying ML models on cloud (e.g., Amazon Sage. Maker or similar)- 3 years developing with SQL, Python, and at least one additional programming language (e.g., Java, Scala, JavaScript, Type. Script)Preferred Qualifications- Experience communicating technical concepts to a non-technical audience- Knowledge of security and compliance standards including HIPAA and GDPR- Knowledge of one or more ML Frameworks (e.g., Py. Torch, Tensor. Flow) and ML methods including NLP models (BERT, GPT-2/3), computer vision-based models (object detection, image recognition), and text-based models (Seq 2 Seq, Topic modeling)- AWS experience preferred, with proficiency in a range of AWS services (e.g., Sage. Maker, Bedrock, EC 2, ECS, EKS, Open. Search, Step Functions, VPC, Cloud. Formation)- Experience with automation (e.g., Terraform, Python), Infrastructure as Code (e.g., Cloud. Formation, CDK), and Containers & CI/ CD Pipelines- Experience building ML pipelines with ML - Ops best practices, including: data preprocessing, model hosting, feature selection, hyperparameter tuning, distributed & GPU training, deployment, monitoring, and retraining- Experience with ML - Ops (e.g., ML - Flow, Kubeflow) and orchestration (e.g., Airflow, AWS Step Functions). Experience building applications using Gen. AI technologies (LL - Ms, Vector Stores, Lang. Chain, Prompt Engineering)