AI Solution Architect

• 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.

• 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.

• 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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