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We are looking for an AI Consultant (Principal AI Engineer) to support the design and adoption of Agentic AI based solutions with our partners and customers. This role is highly architecture focused and partner facing, combining deep systems thinking with practical AI knowledge. You will work closely with external partners to understand their existing architectures and data landscapes, and provide technical consultation on implementing scalable, secure AI solutions.
Job Responsibility:
Partner-facing solution consulting: Engage directly with partners and customers to understand their system architectures, integration patterns, and data environments. Lead technical discovery sessions and act as a trusted advisor on applying Agentic AI solutions within their constraints
Architect AI-enabled solutions: Design end to end architectures for Agentic AI systems, including agent orchestration, data flows, model integration, APIs, and security boundaries. Ensure designs align with partner environments such as cloud, hybrid, or on prem deployments
Translate requirements into blueprints: Convert business and technical requirements into clear solution architectures, reference designs, and implementation guidance that partners can execute against
Guide AI and data integration: Advise on data requirements, data readiness, and integration of AI models with enterprise systems. Provide guidance on patterns such as retrieval augmented generation (RAG), tool using agents, and human in the loop workflows
Define best practices and guardrails: Apply Responsible AI principles, including data governance, security, safety controls, and risk mitigation. Contribute to standards, templates, and reference architectures for repeatable partner deployments
Collaborate with internal teams: Work with product, engineering, and platform teams to align partner needs with product capabilities and roadmap. Support pilots, proofs of concept, and early customer implementations
Technical communication and enablement: Produce architecture diagrams, documentation, and presentations. Clearly explain technical trade offs and architectural decisions to both technical and non technical stakeholders
Stay current on Agentic AI: Track emerging tools, frameworks, and architectural patterns in generative and Agentic AI, and guide partners on practical adoption
Requirements:
Extensive architecture and engineering experience: 12+ years designing and building complex software systems, with strong depth in system and solution architecture
Enterprise solution architecture expertise: Proven experience translating business and technical requirements into scalable architectures involving multiple systems, integrations, and data sources
AI and generative AI familiarity: Solid understanding of AI and ML concepts, with hands on exposure to generative AI and LLM based systems in enterprise contexts
Agentic AI understanding: Familiarity with agent based architectures, orchestration patterns, and enterprise considerations such as guardrails, observability, and control
Partner and consulting mindset: Experience working directly with customers or partners in a consulting, advisory, or solution engineering role. Comfortable influencing architectural decisions
Strong communication skills: Ability to explain complex technical concepts clearly, create effective documentation, and engage senior technical and business stakeholders
Technical leadership: Experience guiding engineering teams through design decisions, reviews, and implementation challenges
Education and fundamentals: Bachelor’s or Master’s degree in Computer Science or a related field, with strong computer science and systems design fundamentals
Software engineering proficiency: Strong programming skills in languages such as Python, Java, or JavaScript/TypeScript. Ability to reason across backend systems, APIs, and data layers
Systems and cloud architecture: Experience designing distributed systems on AWS, Azure, or GCP. Familiar with microservices, event driven architectures, and API centric design
AI and data integration: Working knowledge of AI solution lifecycles, including data preparation, model integration, embeddings, vector databases, and prompt based systems
Security and governance awareness: Understanding of enterprise security, data privacy, and Responsible AI considerations
Analytical problem solving: Ability to evaluate architectural options, assess trade offs, and recommend pragmatic solutions for partner environments
Collaboration and delivery: Experience working in agile environments, collaborating across teams, and supporting solutions from design through early delivery