{"id":3884184,"url":"https://alion.io/job/ares-management-senior-engineer-ai-platform-engineering","title":"Senior Engineer, AI Platform Engineering","company":{"id":704273,"name":"Ares Management","domain":"aresmgmt.com","url":"https://alion.io/company/ares-management","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":77,"open_postings":13,"ghost_share":0,"stale_share":0.923,"repost_share":0,"time_to_fill_p50_days":28,"computed_at":"2026-11-08T05:47:12Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mumbai, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Embeddings","optional":false},{"name":"FinOps","optional":false},{"name":"Function Calling","optional":false},{"name":"Git","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Semantic Search","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Anthropic","optional":true},{"name":"AutoGen","optional":true},{"name":"Databricks","optional":true},{"name":"Delta Lake","optional":true},{"name":"Hybrid Search","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"LlamaIndex","optional":true},{"name":"MLFlow","optional":true},{"name":"OpenAI","optional":true},{"name":"Semantic Kernel","optional":true}],"status":"live","first_seen_at":"2026-11-06T19:08:34Z","employer_posted_date":"2026-11-06","last_verified_at":"2026-11-09T03:44:00Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"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.\nJob Description\nPRIMARY FUNCTIONS & RESPONSIBILITIES\nAI Engineering\nDesign, develop, test, and operate production-grade AI services and applications.\nWrite clean, maintainable, well-tested production code using Python and modern software engineering practices.\nDevelop APIs, services, libraries, and reusable components that support AI applications across the firm.\nParticipate in technical design, code reviews, testing, troubleshooting, and production support.\nWork with senior engineers to implement platform architecture, engineering standards, and reusable design patterns.\nOwn defined engineering components and workstreams from development through deployment and ongoing operation.\nGenerative AI & Shared AI Services\nBuild Retrieval-Augmented Generation capabilities using embeddings, semantic search, hybrid retrieval, vector databases, document processing, and structured data retrieval.\nDevelop reusable services for document extraction, search, retrieval, and AI-powered workflows.\nBuild agentic and multi-step workflows incorporating model reasoning, tools, enterprise data, and application services.\nImplement prompt templates, structured outputs, tool-use patterns, and reusable orchestration components.\nBuild integrations using Model Context Protocol and other AI interoperability patterns where appropriate.\nContribute to evaluation frameworks for measuring retrieval quality, generation accuracy, workflow performance, and overall AI-system quality.\nExperiment with emerging models, frameworks, retrieval techniques, and AI engineering approaches and help translate successful experiments into production capabilities.\nAI Platform Development\nContribute to enterprise AI gateway capabilities supporting model access, routing, resiliency, usage tracking, and cost management.\nDevelop common APIs and SDKs that make AI platform capabilities easy for other engineering teams to consume.\nSupport prompt management, model configuration, model lifecycle, and deployment capabilities.\nIntegrate AI services with enterprise data products, applications, APIs, and internal systems.\nHelp build reusable tooling and reference implementations that accelerate development by application and vertical engineering teams.\nSupport platform capabilities for tracing, observability, usage attribution, and AI FinOps.\nApplication Enablement\nPartner with AI Product Management, Data Engineering, and business-aligned technology teams to translate business requirements into working technical solutions.\nDevelop application-specific AI workflows and integrations using shared enterprise AI platform services.\nEnable secure connectivity between AI applications, enterprise systems, and governed data sources.\nHelp develop AI capabilities supporting workflows across investment and corporate functions.\nWork directly with engineers and product teams to troubleshoot issues, improve solution quality, and move AI use cases into production.\nCreate documentation, examples, and reusable patterns that help other teams successfully adopt shared AI capabilities.\nDevOps, Reliability & Observability\nBuild and maintain CI/CD pipelines for AI applications and services.\nDevelop automated testing for software components, prompts, retrieval pipelines, and AI workflows.\nImplement monitoring, telemetry, logging, tracing, and operational-health capabilities.\nTroubleshoot production issues and contribute to root-cause analysis and continuous improvement.\nSupport deployment processes, rollback mechanisms, production-support models, and reliability standards.\nMonitor application and service performance, latency, quality, usage, and cost.\nSecurity, Risk & Governance\nFollow secure engineering practices for authentication, authorization, secrets management, encryption, and data access.\nImplement entitlement-aware access to enterprise data and AI services.\nPartner with senior engineers to meet cybersecurity, data-protection, information-barrier, and AI governance requirements.\nContribute documentation and technical evidence required for architecture, security, and governance reviews.\nBuild solutions consistent with the firm's engineering, cybersecurity, regulatory, compliance, and risk-management standards.\nCollaboration & Engineering Culture\nCollaborate closely with AI engineers, data engineers, product managers, application developers, cybersecurity teams, and business stakeholders.\nParticipate actively in design reviews, code reviews, technical discussions, and engineering planning.\nShare knowledge and reusable patterns with other engineers.\nHelp improve engineering standards, development practices, documentation, and tooling.\nDemonstrate a strong ownership mindset and a focus on software quality, simplicity, reliability, and continuous learning.\nQUALIFICATIONS\nEducation\nBachelor's degree in Computer Science, Engineering, Mathematics, Data Science, or a related technical discipline.\nExperience Required\n4 to 8 years of professional software engineering, data engineering, machine learning engineering, or related experience.\nHands-on experience developing machine learning, Generative AI, LLM, or data-intensive applications.\nStrong Python development skills and experience writing production-quality software.\nExperience designing or consuming APIs, services, and cloud-based applications.\nExperience with a major cloud platform, preferably Azure.\nPractical experience with one or more Generative AI techniques such as RAG, embeddings, vector search, prompt engineering, structured generation, tool use, or agentic workflows.\nFamiliarity with modern software development practices including Git, automated testing, CI/CD, containers, and production monitoring.\nStrong problem-solving and debugging skills.\nAbility to take ownership of defined technical components and deliver them from design through production.\nStrong written and verbal communication skills and the ability to work effectively in a collaborative engineering environment.\nPreferred Qualifications\nExperience with Databricks, including Delta Lake, Unity Catalog, Model Serving, MLflow, or Vector Search.\nExperience with Azure OpenAI, Azure AI services, OpenAI, Anthropic, or comparable enterprise LLM providers.\nExperience with LLM application or agent frameworks such as LangGraph, LangChain, LlamaIndex, Semantic Kernel, AutoGen, or comparable technologies.\nExperience building RAG applications, document-processing pipelines, semantic or hybrid search, or vector-search solutions.\nFamiliarity with Model Context Protocol, function calling, tool-use patterns, or agent interoperability standards.\nExperience with model or application evaluation, including automated evaluation or LLM-as-judge approaches.\nExperience with LLM observability, tracing, production monitoring, or AI cost-management practices.\nExperience building applications against governed enterprise data.\nExperience working in financial services or another regulated environment.\nAsset management, private equity, investment banking, or alternative investments experience.\nReporting Relationships\nHead of Data and AnalyticsThere is no set deadline to apply for this job opportunity. 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