Position Overview
Apollo and Athene are together building a strategic growth hub in Austin — a new office incubating emerging businesses across our combined asset management and retirement solutions platform, spanning product, distribution, infrastructure, and market-making. The Austin team offers the opportunity to build from the ground up with the backing of both firms' scale. This role will be based in Austin.
Apollo is seeking an Engineering Lead to lead the build-out and delivery of the firm’s enterprise AI engineering capabilities. Reporting to the Head of Innovation and AI Engineering, this individual will be responsible for architecting, developing, and deploying scalable AI solutions that directly enhance investment decision-making, portfolio intelligence, risk management, and firmwide productivity. This role requires deep technical expertise combined with strong commercial judgment. The successful candidate will lead hands-on engineering efforts while managing a high-performing team, ensuring that AI initiatives move beyond proof-of-concept and into secure, production-grade platforms that drive measurable business impact.
Primary Responsibilities:
- Lead the engineering design, build, and deployment of Apollo’s AI platforms, including large language model (LLM) infrastructure, retrieval-augmented generation (RAG) frameworks, model orchestration layers, and secure API services.
- Oversee the end-to-end AI development lifecycle, including data ingestion, feature engineering, model integration, testing, validation, monitoring, and production support.
- Write high-quality, production-grade code (primarily in Python) and establish engineering standards for maintainability, performance, and security.
- Lead the full AI system lifecycle: data ingestion, preprocessing, feature engineering, model integration, evaluation, deployment, monitoring, and optimization.
- Evaluate, fine-tune, and deploy foundation models; implement vector databases and embedding pipelines; optimize prompt engineering and context management strategies.
- Establish engineering best practices for MLOps, version control, model observability, drift detection, explainability, and lifecycle management.
- Partner with Cybersecurity, Infrastructure, Data, Risk, and Legal & Compliance teams to ensure AI systems meet regulatory, data privacy, and information security standards.
- Evaluate and implement third-party AI technologies and cloud services where appropriate, balancing build-versus-buy decisions with long-term strategic value.
- Mentor and develop a team of AI engineers and applied data scientists, fostering a culture of technical rigor, intellectual curiosity, and accountability.
- Stay abreast of advancements in machine learning, generative AI, and enterprise AI tooling, applying emerging capabilities in a disciplined and commercially pragmatic manner.
Qualifications & Experience
- 10+ years of experience in software engineering, machine learning engineering, or applied AI, with significant experience delivering production AI systems in complex enterprises.
- Demonstrated leadership experience managing engineering teams in financial services, asset management, banking, insurance, or similarly regulated environments.
- Deep expertise in Python and modern AI/ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex, or similar).
- Demonstrated experience deploying LLM-powered applications in production, including RAG architectures, vector databases, prompt engineering, and API orchestration.
- Strong understanding of distributed systems, data engineering pipelines, and cloud-native architectures (AWS, Azure, or GCP).
- Proven experience implementing MLOps best practices, including model versioning, CI/CD, monitoring, and performance optimization.
- Proven ability to partner effectively with senior business stakeholders and translate technical concepts into practical applications.
- Bachelor’s degree in computer science, Engineering, Mathematics, or a related technical discipline; advanced degree preferred or equivalent experience.
Preferred
- Experience supporting alternative asset managers, global investment banks, insurance platforms, or large institutional investors.
- Demonstrated success delivering AI-enabled tools for investment research, document intelligence, financial modeling, or workflow automation.
- Familiarity with foundation model fine-tuning, vector databases, knowledge graph integration, and enterprise search architectures.
- Experience scaling small, high-impact engineering teams within fast-growing organizations.
- External engagement in the AI community, including publications, open-source contributions, or industry speaking engagements.
OUR BENEFITS
Apollo is committed to providing innovative and competitive benefits that support the wellbeing of our employees and their families around the globe.
The base salary range for this position is :
This position is also eligible for a discretionary annual bonus based on personal, team, and Firm performance. Compensation ranges are based on several factors including job function, level, and geographic location. Final offer amounts are determined by multiple factors including candidate experience and expertise, and may vary from the amounts listed here.
