Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A leading global professional services organization is expanding its AI & Engineering team to help enterprise clients accelerate AI adoption and digital transformation. The team specializes in building, deploying, and operating AI-powered, cloud-native, and data-driven solutions that modernize mission-critical operations across industries. By combining advanced engineering expertise with deep industry knowledge, the organization delivers scalable AI platforms that enable clients to achieve measurable business outcomes.
Key Responsibilities
- Partner with clients to identify business challenges and translate high-value Generative AI opportunities into scalable solutions.
- Collaborate with business leaders, product owners, architects, and engineering teams to align project priorities and delivery goals.
- Lead client workshops and technical working sessions to design AI-powered solutions that deliver measurable business value.
- Rapidly prototype, develop, and deploy production-ready Generative AI applications.
- Contribute independently within an engineering delivery pod while mentoring junior engineers.
- Design and build AI-enabled applications, agentic platforms, and enterprise AI workflows.
- Develop scalable AI engineering patterns, reusable frameworks, and human-in-the-loop solution architectures.
- Apply architectural best practices that balance performance, scalability, security, cost efficiency, and model governance.
- Deliver high-quality software following modern engineering practices, including testing, CI/CD, logging, version control, and documentation.
- Design extensible software solutions while supporting sprint planning, estimation, and technical delivery.
- Build reusable assets such as code libraries, prompt templates, runbooks, and reference implementations.
- Collaborate with cross-functional and distributed engineering teams to deliver enterprise AI solutions.
Required Qualifications
- Bachelor’s degree (or equivalent) in Computer Science, Data Science, Engineering, or a related field.
- 7+ years of experience in software engineering, data engineering, data science, or analytics engineering.
- 1+ year of hands-on experience designing and deploying Generative AI or LLM-powered applications in production or client environments.
- 1+ year of experience with AWS AI & Data services, including one or more of the following:
- Amazon Bedrock
- Bedrock AgentCore
- Strands Agents SDK
- Knowledge Bases
- Guardrails
- 1+ year of experience working with AWS Neptune and OpenSearch.
- Experience leading technical workstreams and translating business requirements into AI-powered solutions.
- Possession of at least three of the following AWS certifications:
- AWS Certified Cloud Practitioner
- AWS Certified Solutions Architect – Associate
- AWS Certified AI Practitioner (AIF-C01)
- AWS Certified Generative AI Developer – Professional (AIP-C01)
- AWS Certified Machine Learning Engineer – Associate (MLA-C01)
- AWS Certified Data Engineer – Associate
- Experience developing reliable, maintainable, and well-documented software.
- Willingness to travel approximately 50% based on client and project requirements.
- Eligibility to work with limited immigration sponsorship where applicable.
Preferred Qualifications
- Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
- Experience working directly with enterprise client technical teams in fast-paced consulting or delivery environments.
- Background in data engineering technologies including Spark, Airflow, dbt, streaming platforms, or data modeling.
- Experience with machine learning, experimentation, feature engineering, or model evaluation.
- Knowledge of MLOps or LLMOps practices, including evaluation frameworks, model monitoring, and prompt management.
- Experience fine-tuning models using Amazon SageMaker (SFT and RFT).
- Experience integrating LLM applications with enterprise systems through APIs, microservices, or event-driven architectures.
- Experience working with hybrid onshore and offshore engineering teams.
- Familiarity with enterprise security, privacy, governance, and regulatory compliance best practices.
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