Forward Deployed AI Engineer

Key Responsibilities Delivery & Execution • Lead end-to-end solution delivery across Corporate, Compliance, Legal, and Risk Management domains — from design and build through UAT and production deployment • Own release readiness: manage UAT-to-production transitions, validate operational quality, and ensure solutions meet the firm's engineering and compliance standards • Build and maintain CI/CD pipelines and DevOps processes on Apollo's Azure-based platform, working within established GitOps and Flux deployment patterns • Manage delivery across multiple concurrent workstreams, coordinating with engineering, product, and business teams to maintain momentum and remove blockers AI-Enabled Solution Design & Build • Apply AI tools, agent frameworks, and workflow orchestration to solve business-critical problems — selecting the right approach and model for each use case rather than defaulting to the most complex solution • Design and implement agent workflows on Apollo's Axon SDK and Azure AI Foundry, integrating Azure OpenAI, Document Intelligence, and AI Search • Build multi-step automations and AI pipelines using Temporal as the firm's enterprise workflow orchestrator • Integrate solutions with Apollo's data platform — consuming Snowflake data through the Delphi data PaaS, connecting to operational systems, and working within the firm's data governance model • Expose and consume capabilities through Apollo's internal Model Context Protocol (MCP) broker pattern Stakeholder Engagement • Work directly with business leads, product managers, and domain teams to gather requirements, shape use cases, and translate them into clear, deliverable technical solutions • Communicate progress, risks, and trade-offs clearly to both technical leads and senior business stakeholders • Contribute to solution documentation, runbooks, and knowledge transfer as a standard part of delivery Required Skills & Experience Engineering & Delivery • Proven track record of end-to-end solution delivery in production enterprise environments — design through deployment and ongoing operation • Strong hands-on experience with Azure: AKS, Azure AI services, CI/CD pipeline implementation, and DevOps practices • Solid understanding of GitOps deployment patterns and Flux-based release workflows • Experience writing infrastructure configuration — Terraform or equivalent IaC — in a team- managed environment • Comfortable working across the full delivery lifecycle including UAT coordination, operational readiness, and post-launch support AI & Automation • Deep practical experience with AI development workflows: agent design, prompt engineering, workflow orchestration, and model selection • Hands-on experience with AI frameworks and orchestration tools — applied to real delivery problems, not proof-of-concept work • Fluency with AI-assisted development tooling (Cursor, Claude, Codex) as a primary accelerant for engineering delivery • Working knowledge of Azure AI Foundry, Azure OpenAI, Document Intelligence, and AI Search is strongly preferred • Experience with Temporal or a comparable workflow orchestration platform is a plus Domain & Environment • Exposure to Corporate, Compliance, Legal, or Risk Management functions; understanding of the domain accelerates delivery significantly • Strong communication and stakeholder management skills; ability to operate credibly at both working and senior levels • Ownership mindset, high accountability, and the flexibility to meet delivery commitments in a demanding environment The Client Platform AI & Agents Axon SDK · Azure AI Foundry · Azure OpenAI · Document Intelligence · AI Search · MCP broker Orchestration Temporal — agent runs, ETL pipelines, multi-step automations Data Snowflake via Delphi · Event Hubs · ADLS Gen2 · Cosmos DB · Redis · EDB Postgres Cloud & Deployment Azure · AKS · Flux GitOps · GitHub Actions · Terraform central module catalog Observability Datadog · Prometheus / Grafana · PagerDuty ITSM & Identity ServiceNow · Jira · Okta (OIDC) · Active Directory