Principal, AI Platform Engineering

Company: Ares Operations
Location: New York City
Posted on: April 1, 2026

Job Description:

Over the last 20 years, Ares’ success has been driven by our people and our culture. Today, our team is guided by our core values – Collaborative, Responsible, Entrepreneurial, Self-Aware, Trustworthy – and our purpose to be a catalyst for shared prosperity and a better future. Through our recruitment, career development and employee-focused programming, we are committed to fostering a welcoming and inclusive work environment where high-performance talent of diverse backgrounds, experiences, and perspectives can build careers within this exciting and growing industry. Job Description Overview We are seeking an exceptional Principal AI Platform Engineer to design and build an enterprise-grade generative AI platform from the ground up. This is a leadership role that combines deep technical expertise in AI systems architecture with the strategic vision to shape how our organization scales AI capabilities across all business domains. You will architect a comprehensive platform spanning model gateways, retrieval services, model registries, prompt libraries, and deployment pipelines—enabling teams across the firm to build, deploy, and operationalize AI applications with confidence, compliance, and security. Key Responsibilities Platform Architecture & Design Design and build a foundational AI platform that enables secure, scalable, and compliant generative AI across the enterprise Architect multi-LLM gateway capabilities to support diverse model providers, allowing teams to leverage best-of-breed models for different use cases Establish platform standards and patterns that balance flexibility, safety, governance, and performance Core Platform Components Develop multi-LLM gateway: unified interface for accessing multiple LLM providers with load balancing, fallback handling, and cost optimization Build RAG (Retrieval-Augmented Generation) retrieval services: enterprise search, semantic indexing, and document retrieval at scale Create model registry and governance: centralized catalog of models, versions, fine-tuning metadata, performance metrics, and compliance tracking Design prompt library and version control: organizational repository for prompts with testing, evaluation, and A/B testing capabilities Implement Model Context Protocol (MCP) gateway: enable secure integration between AI applications and external tools, APIs, and data sources Build FinOps infrastructure: cost tracking, optimization, and allocation across models, usage patterns, and business units Agent-to-Agent (A2A) Workflows Design orchestration framework for complex, multi-step AI workflows across applications Enable reliable, scalable execution of chained AI operations with state management and error recovery Integrate with broader data ecosystem for workflow triggers and data pipelines Data Gateway Integration Partner with data platform teams to design AI-native data access patterns Enable secure, governed access to enterprise data and RAG and model training Build metadata and lineage tracking for AI-consumed data Deployment & DevOps Design sandbox-to-production pipelines: safe, repeatable processes for testing and deploying AI applications Implement CI/CD for AI models: versioning, testing, promotion, and rollback capabilities Build observability and monitoring: telemetry, performance metrics, cost tracking, and compliance auditing Establish disaster recovery and high-availability patterns Collaboration & Enablement Work closely with Data Products team to align platform capabilities with data governance and analytics infrastructure Partner with AI Enablement teams to provide tools, SDKs, documentation, and best practices that democratize AI development Lead technical discussions on platform strategy, roadmap, and trade-offs across the organization Build internal developer experience and platform adoption Security Architecture & Implementation Design and implement comprehensive security architecture aligned with firm cyber and information security guidelines Build authentication and authorization frameworks: role-based access control (RBAC), attribute-based access control (ABAC), and service-to-service authentication Implement encryption standards: encryption at rest (AES-256 or equivalent) and in transit (TLS 1.2) for all sensitive data Design secure API gateways and service boundaries with rate limiting, request validation, and DDoS protection Implement secrets management: secure storage and rotation of credentials, API keys, and certificates Build comprehensive audit logging and monitoring: all access, modifications, and security events logged with immutable audit trails Partner with Infosec and Security Operations to implement continuous security monitoring and threat detection Governance, Compliance & Risk Management Ensure platform compliance with regulatory requirements: SOC 2 Type II, data residency, and audit trails Implement data governance: classify data sensitivity levels, enforce data handling policies, and ensure appropriate access controls Build model governance: track model provenance, versioning, training data lineage, and approval workflows for production deployment Prevent data exfiltration and prompt injection attacks through input validation, output filtering, and rate limiting Establish responsible AI practices: bias detection, fairness assessment, and explainability requirements Manage third-party vendor security: assess LLM provider security postures, data processing agreements, and compliance certifications Create model risk assessment framework: evaluate models for regulatory, market, and operational risks before production deployment Work with Compliance, Legal, and Risk teams to ensure platform meets all governance requirements and documentation standards Required Qualifications 10 years of software engineering experience, with 5 years building large-scale, distributed systems or platform infrastructure 3 years of hands-on experience with generative AI, LLMs, RAG systems, or AI infrastructure—either in production systems or applied research Deep expertise in one or more: Python, Go, Rust, or Java; experience building APIs and orchestration systems Strong understanding of LLM architectures, prompting strategies, fine-tuning, and RAG design patterns Demonstrated experience with: model serving (vLLM, Ollama, TensorFlow Serving), vector databases, and embedding models Proficiency in cloud platforms (AWS, GCP, Azure) and containerization/orchestration (Docker, Kubernetes) Experience designing and building multi-tenant, secure platform systems with strong governance and observability Demonstrated expertise in security: architecture, secure coding practices, authentication/authorization, encryption, and threat modeling Experience with compliance frameworks and security certifications: SOC 2, ISO 27001, GDPR, or similar Track record of leading technical initiatives from architecture through production deployment Excellent communication skills; ability to explain complex technical and security concepts to executives and cross-functional teams Preferred Qualifications Experience in financial services, private equity, or alternative assets technology environments Familiarity with LangChain, LlamaIndex, or similar AI orchestration frameworks Experience with MLOps tools and practices: model versioning, feature stores, experiment tracking Knowledge of eval frameworks, retrieval evaluation, or AI model benchmarking Experience with data governance platforms or metadata management systems Experience building zero-trust architectures or implementing security controls in cloud-native environments Contributions to open-source AI/ML projects or publications in the AI/ML space Experience in building developer platforms or internal tools that drive organizational adoption Reporting Relationships Partner, Chief Information Officer Compensation The anticipated base salary range for this position is listed below. Total compensation may also include a discretionary performance-based bonus. Note, the range takes into account a broad spectrum of qualifications, including, but not limited to, years of relevant work experience, education, and other relevant qualifications specific to the role. $300,000 - $350,000 The firm also offers robust Benefits offerings. Ares U.S. Core Benefits include Comprehensive Medical/Rx, Dental and Vision plans; 401(k) program with company match; Flexible Savings Accounts (FSA); Healthcare Savings Accounts (HSA) with company contribution; Basic and Voluntary Life Insurance; Long-Term Disability (LTD) and Short-Term Disability (STD) insurance; Employee Assistance Program (EAP), and Commuter Benefits plan for parking and transit. Ares offers a number of additional benefits including access to a world-class medical advisory team, a mental health app that includes coaching, therapy and psychiatry, a mindfulness and wellbeing app, financial wellness benefit that includes access to a financial advisor, new parent leave, reproductive and adoption assistance, emergency backup care, matching gift program, education sponsorship program, and much more. There is no set deadline to apply for this job opportunity. Applications will be accepted on an ongoing basis until the search is no longer active.

Keywords: Ares Operations, Hempstead , Principal, AI Platform Engineering, IT / Software / Systems , New York City, New York