Senior AI Engineer

We are seeking a Senior AI Engineer who can operate at the intersection of AI architecture, engineering, and client engagement. This role is ideal for someone who can bridge agentic AI systems and classical ML/data systems, while confidently guiding clients through technical decision-making and solution design. You will play a key role in building next-generation AI platforms and data-driven systems within the asset and wealth management domain, working closely with stakeholders to translate business needs into scalable technical solutions. Key Responsibilities Act as a customer-facing AI expert, engaging directly with clients to understand requirements and define solutions Design and implement AI/ML architectures, including both: Agentic AI systems (LLMs, copilots, autonomous workflows) Classical ML/data pipelines Lead technical decision-making, including selecting appropriate tools, frameworks, and infrastructure Build and contribute to AI platforms (AIP) and AI-driven SDLC workflows Develop scalable data solutions across the data supply chain (ingestion, transformation, modeling, serving) Collaborate with cross-functional teams including data engineers, software engineers, and business stakeholders Communicate complex technical concepts clearly to non-technical and executive audiences Required Qualifications 5+ years of experience in AI/ML engineering, data engineering, or related fields Strong experience with: LLMs / Generative AI / agentic workflows Traditional ML models and data systems Proficiency in modern programming languages such as Python Experience with cloud platforms (AWS, Azure, or GCP) Solid understanding of data architectures and pipelines Ability to evaluate and recommend technologies (e.g., relational vs graph databases like Postgres vs Neo4j) Strong communication and client-facing skills Preferred Qualifications Experience in financial services, asset management, or wealth management Background in AI platform development or enterprise AI transformation Familiarity with tools/platforms in the modern AI ecosystem (e.g., vector databases, orchestration frameworks, model deployment tools) Experience working in consulting or advisory roles Full-stack capabilities across AI, backend, and data systems What Success Looks Like Delivering high-quality AI solutions that align with business goals Acting as a trusted advisor to clients on AI strategy and implementation Rapidly translating ambiguous requirements into clear technical direction Contributing to scalable, production-grade AI systems

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