Assistant General Manager, AI Engineering

Role Overview

We seek an Assistant General Manager to spearhead our forward-deployed AI engineering team, working closely with business units to develop and scale AI solutions that achieve well-defined business goals. Reporting to the Head of Data & AI, this role is responsible for end-to-end solution delivery, maintaining engineering and architectural standards, ensuring system reliability, and nurturing a high-performing AI engineering workforce.

Key Responsibilities

• Collaborate with business stakeholders to comprehend workflows, test hypotheses, and set measurable success criteria.
• Lead AI solution lifecycles from initial exploration and prototyping to production rollout, user adoption, ongoing support, and creation of reusable components for shared use.
• Direct the design and implementation of AI services, APIs, and user workflows, incorporating large language models (LLMs), retrieval systems, and tool execution mechanisms.
• Integrate enterprise applications via secure APIs and Multi-Cloud Platforms, selecting appropriate methods such as deterministic workflows, agents, or traditional machine learning models depending on the scenario.
• Engineer context mechanisms including retrieval, chunking, embeddings, reranking, and assembly while managing instructions, tool definitions, conversation states, memory, freshness, token budgeting, provenance tracking, and access controls.
• Develop runtime agent frameworks addressing tool contracts, orchestration, state persistence, checkpoints, bounded execution, retries, timeouts, and recovery, preventing duplicated side effects and imposing approval boundaries for critical operations.
• Establish evaluation datasets and regression gates to monitor the impact of model, prompt, retrieval, and tool adjustments, perform failure analysis, and oversee metrics such as task success, latency, cost, and groundedness.
• Lead release processes, rollbacks, and incident investigations alongside platform teams.
• Collaborate with Data Engineering to build robust data ingestion and transformation pipelines, maintain data quality, schema integrity, and lineage.
• Apply deep learning and conventional machine learning approaches with rigorous validation and continuous monitoring.
• Define technology architecture and coding standards, prioritize projects in alignment with business needs, and ensure compliance with cybersecurity requirements including identity management, least privilege, secrets handling, audit trails, and defenses against prompt injections.
• Promote rigorous code testing, review processes, and accountable release management when employing coding agents.
• Manage and mentor AI engineering staff through goal-setting, resource allocation, continuous feedback, performance evaluations, and career coaching to foster accountability and team ownership.

Candidate Requirements

• A bachelor’s degree in disciplines such as Computer Science, Computer Engineering, Software Engineering, Data Science, Artificial Intelligence, or related fields.
• At least 10 years of professional experience in technology or software engineering with a minimum of 5 years delivering AI and machine learning solutions, including deployment of LLM-based applications or agents in production environments.
• Expertise in LLM engineering concepts like retrieval-augmented generation (RAG), embeddings, structured data outputs, tool invocation, and context management.
• Proficiency in Python and SQL programming, API development, system integrations, automated testing, version control with Git, continuous integration/deployment pipelines, containerization, authentication protocols, and debugging in production settings.
• Experience with AI evaluation methodologies, failure analysis, reliability monitoring, data pipeline architecture, database management, and secured retrieval systems with access control.
• Knowledge of cloud-based deployments, relational and analytical data stores, model serving APIs, and automated delivery pipelines; familiarity with comparable technologies or frameworks is a plus.
• Solid foundation in machine learning and deep learning, including understanding transformer architectures, embeddings, model validation techniques, and the strategic choice among prompting, retrieval, fine-tuning, and classical ML methods.
• Ability to convert business challenges into quantifiable outcomes, oversee technical architecture and solution delivery, establish engineering best practices, and coordinate cross-functional teams spanning business, technology, and security.
• Strong history of partnership with business users and product owners, collaborating effectively with data engineering, infrastructure, operations, security, architecture, and application teams to deploy and manage integrated AI solutions.
• Demonstrated leadership in guiding AI engineering groups, setting strategic objectives, managing resources efficiently, nurturing talent, providing mentorship, and driving team performance.
• Familiarity with PyTorch, open-weight models, MLOps frameworks, multimodal or document AI solutions, Multi-Cloud Platforms, or leading AI services such as Azure OpenAI, OpenAI, Anthropic, AWS, and Google AI platforms is beneficial.