Lead AI Engineer

Join a cutting-edge initiative within the Digital Channel division of one of the world’s largest asset management firms. This role focuses on enhancing Generative AI–powered applications and driving experimentation across emerging AI capabilities. You’ll work on enterprise-scale AI solutions that serve millions of end users, leveraging the latest advancements in LLMs, multimodal AI, and agentic architectures.

Why Join Us?

• Be part of a global leader in asset management
• Work on enterprise-scale Generative AI applications with real-world impact
• Innovate at the forefront of AI technologies, including LLMs, multimodal AI, and agentic architectures
• Collaborate within a completely cloud-native environment with a strong Google Cloud and enterprise AI focus
• Take ownership of projects from experimentation and prototyping to production deployment

Req: 1033174174

Implement and optimize voice-enabled features using multimodal LLMs or third-party services (e.g., ElevenLabs, Whisper, Google Speech APIs) Design and build memory architectures for session-based, short-term, and long-term memory (e.g., RAG pipelines, vector stores, conversation buffers) Experiment with Google enterprise AI offerings (e.g., GECX and related services) to assess their suitability for production workloads Contribute to rapid prototyping, proof-of-concept development, and technical evaluations in a fast-paced, exploratory environment Strong programming skills in Python (primary); experience in Java/TypeScript is a plus Solid understanding of data structures, algorithms, and software engineering fundamentals Experience building production-grade APIs and services (REST, async, scalable patterns) Familiarity with Git, CI/CD pipelines, and Agile delivery methodologies Hands-on experience with LLMs (e.g., prompt engineering, embeddings, summarization, Q&A) Proficiency with ML frameworks such as PyTorch, TensorFlow, or equivalent Understanding of model evaluation, performance metrics, and error analysis Experience fine-tuning, orchestrating, or integrating foundation models (vendor or open-source) Experience with Retrieval-Augmented Generation (RAG) design and implementation Proficiency with vector databases/search (e.g., embeddings, similarity search, metadata filtering) Knowledge of ingestion pipelines for structured and unstructured data Familiarity with tool/function calling and agent-style workflows Cloud-native development experience on AWS, Azure, or GCP (Google Cloud preferred) Proficiency with containers and orchestration tools (e.g., Docker, Kubernetes) Experience with event-driven and distributed systems Observability expertise: logging, metrics, tracing, and model monitoring Understanding of Responsible AI principles, including privacy, bias, explainability, and safety Experience working with enterprise controls: security reviews, data protection, and compliance Familiarity with model risk management, auditability, and guardrails Ability to operationalize AI safely in regulated environments