Senior Engineer, AI Platform Engineering

Overview

Senior Engineer on the AI Platform Engineering team at Ares Management, building production-grade AI services, shared platform capabilities and application enablement across the firm.

What you'll do

• Design, develop, test, and operate production-grade AI services and applications.
• Write clean, maintainable, well-tested production code using Python and modern software engineering practices.
• Develop APIs, services, libraries, and reusable components that support AI applications across the firm.
• Participate in technical design, code reviews, testing, troubleshooting, and production support.
• Work with senior engineers to implement platform architecture, engineering standards, and reusable design patterns.
• Own defined engineering components and workstreams from development through deployment and ongoing operation.
• Build Retrieval-Augmented Generation capabilities using embeddings, semantic search, hybrid retrieval, vector databases, document processing, and structured data retrieval.
• Develop reusable services for document extraction, search, retrieval, and AI-powered workflows.
• Build agentic and multi-step workflows incorporating model reasoning, tools, enterprise data, and application services.
• Implement prompt templates, structured outputs, tool-use patterns, and reusable orchestration components.
• Build integrations using Model Context Protocol and other AI interoperability patterns where appropriate.
• Contribute to evaluation frameworks for measuring retrieval quality, generation accuracy, workflow performance, and overall AI-system quality.
• Experiment with emerging models, frameworks, retrieval techniques, and AI engineering approaches and help translate successful experiments into production capabilities.
• Contribute to enterprise AI gateway capabilities supporting model access, routing, resiliency, usage tracking, and cost management.
• Develop common APIs and SDKs that make AI platform capabilities easy for other engineering teams to consume.
• Support prompt management, model configuration, model lifecycle, and deployment capabilities.
• Integrate AI services with enterprise data products, applications, APIs, and internal systems.
• Help build reusable tooling and reference implementations that accelerate development by application and vertical engineering teams.
• Support platform capabilities for tracing, observability, usage attribution, and AI FinOps.
• Partner with AI Product Management, Data Engineering, and business-aligned technology teams to translate business requirements into working technical solutions.
• Develop application-specific AI workflows and integrations using shared enterprise AI platform services.
• Enable secure connectivity between AI applications, enterprise systems, and governed data sources.
• Help develop AI capabilities supporting workflows across investment and corporate functions.
• Work directly with engineers and product teams to troubleshoot issues, improve solution quality, and move AI use cases into production.
• Create documentation, examples, and reusable patterns that help other teams successfully adopt shared AI capabilities.
• Build and maintain CI/CD pipelines for AI applications and services.
• Develop automated testing for software components, prompts, retrieval pipelines, and AI workflows.
• Implement monitoring, telemetry, logging, tracing, and operational-health capabilities.
• Troubleshoot production issues and contribute to root-cause analysis and continuous improvement.
• Support deployment processes, rollback mechanisms, production-support models, and reliability standards.
• Monitor application and service performance, latency, quality, usage, and cost.
• Follow secure engineering practices for authentication, authorization, secrets management, encryption, and data access.
• Implement entitlement-aware access to enterprise data and AI services.
• Partner with senior engineers to meet cybersecurity, data-protection, information-barrier, and AI governance requirements.
• Contribute documentation and technical evidence required for architecture, security, and governance reviews.
• Build solutions consistent with the firm's engineering, cybersecurity, regulatory, compliance, and risk-management standards.
• Collaborate closely with AI engineers, data engineers, product managers, application developers, cybersecurity teams, and business stakeholders.
• Participate actively in design reviews, code reviews, technical discussions, and engineering planning.
• Share knowledge and reusable patterns with other engineers.
• Help improve engineering standards, development practices, documentation, and tooling.
• Demonstrate a strong ownership mindset and a focus on software quality, simplicity, reliability, and continuous learning.

What you'll need

• Bachelor's degree in Computer Science, Engineering, Mathematics, Data Science, or a related technical discipline.
• 4 to 8 years of professional software engineering, data engineering, machine learning engineering, or related experience.
• Hands-on experience developing machine learning, Generative AI, LLM, or data-intensive applications.
• Strong Python development skills and experience writing production-quality software.
• Experience designing or consuming APIs, services, and cloud-based applications.
• Experience with a major cloud platform, preferably Azure.
• Practical experience with one or more Generative AI techniques such as RAG, embeddings, vector search, prompt engineering, structured generation, tool use, or agentic workflows.
• Familiarity with modern software development practices including Git, automated testing, CI/CD, containers, and production monitoring.
• Strong problem-solving and debugging skills.
• Ability to take ownership of defined technical components and deliver them from design through production.
• Strong written and verbal communication skills and the ability to work effectively in a collaborative engineering environment.

Nice to have

• Experience with Databricks, including Delta Lake, Unity Catalog, Model Serving, MLflow, or Vector Search.
• Experience with Azure OpenAI, Azure AI services, OpenAI, Anthropic, or comparable enterprise LLM providers.
• Experience with LLM application or agent frameworks such as LangGraph, LangChain, LlamaIndex, Semantic Kernel, AutoGen, or comparable technologies.
• Experience building RAG applications, document-processing pipelines, semantic or hybrid search, or vector-search solutions.
• Familiarity with Model Context Protocol, function calling, tool-use patterns, or agent interoperability standards.
• Experience with model or application evaluation, including automated evaluation or LLM-as-judge approaches.
• Experience with LLM observability, tracing, production monitoring, or AI cost-management practices.
• Experience building applications against governed enterprise data.
• Experience working in financial services or another regulated environment.
• Asset management, private equity, investment banking, or alternative investments experience.

Details

• Reports to the Head of Data and Analytics.
• Location: Mumbai, India.