AI Solutions Architect
If you are a senior AI and software engineering professional with a strong background in designing and delivering production-grade AI systems, we invite you to join Accenture's fast-growing Artificial Intelligence Platform and work as part of a worldwide team of experts. In this role, you will shape the architecture of advanced AI solutions across multiple client engagements, working closely with delivery teams, product stakeholders, and business leaders. You will define end-to-end scalable, reliable and cost-efficient AI architectures, contribute to technical solutioning in business development initiatives and help evolve our platform's AI engineering practices.
Our future colleague will do
- Define end-to-end scalable, resilient and efficient architectures for advanced AI systems (including data pipelines, model training and serving, retrieval systems, monitoring, and governance) in multi-stakeholder environments, balancing client needs, technical constraints, timelines, and costs;
- Collaborate closely with delivery teams to translate architectural designs into work items and identify and resolve architectural challenges during project execution;
- Own or contribute to technical solutioning in business development initiatives, including proposal input, architecture design, and effort estimation;
- Mentor senior engineers and contribute to raising architectural and engineering maturity across teams;
- Pilot and evaluate emerging tools and AI technologies.
What will help you succeed
- Strong experience designing and delivering production-grade AI software systems across different domains;
- Proven expertise in designing and/or implementing generative AI systems in production, including agentic systems, RAG pipelines, tool-use patterns, and model adaptation strategies (e.g., fine-tuning of open-source LLMs or diffusion models);
- Practical experience in retrieval and knowledge systems, e.g., Vector DBs (Pinecone, Chroma, etc.), ingestion frameworks (Kafka, Debezium, etc.), Graph DBs (AWS Neptune, Neo4j);
- Breadth of knowledge across modern AI technologies, including generative models, deep learning, and predictive ML;
- Solid knowledge of cloud-based AI platforms, infrastructure concepts, and MLOps practices, including containerization, orchestrated and/or serverless deployment, monitoring, versioning and governance;
- Strong client-facing communication skills, with the ability to reason clearly about trade-offs involving performance, cost, risk, and maintainability;
- Collaborative mindset and mentorship-oriented approach to technical leadership.
What we expect
- 6+ years of professional software engineering experience, including work on distributed, large-scale systems;
- 3+ years of hands-on experience in data science and machine learning projects delivered into production;
- Affinity for multi-layered AI system design in various contexts and use cases (e.g., end-to-end AI systems, Agentic platforms, workflow engines, etc.);
- Experience using cloud-based AI platforms (e.g., Azure ML, AWS SageMaker, or equivalent);
- Working knowledge of MLOps methodologies, including CI/CD, monitoring, and governance;
- Proven progression from hands-on implementation to system-level architectural responsibility.
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