{"id":2418849,"url":"https://alion.io/job/cai-senior-ai-engineer","title":"Senior AI Engineer","company":{"id":1819291,"name":"CAI","domain":"cai.io","url":"https://alion.io/company/cai-io","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":75,"open_postings":10,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-14T06:34:16Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Manila, Philippines"],"countries":["PH"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":22000,"max_usd":57000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1986},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":".NET","optional":false},{"name":"Agentic Workflows","optional":false},{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon ECS","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon EventBridge","optional":false},{"name":"Amazon S3","optional":false},{"name":"API Gateway","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"AWS Lambda","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Datadog","optional":false},{"name":"Docker","optional":false},{"name":"Hallucination","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Hybrid Search","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenSearch","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Reranking","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Search","optional":false},{"name":"Semantic Search","optional":false},{"name":"Snyk","optional":false},{"name":"SRE","optional":false},{"name":"Terraform","optional":false},{"name":"Wiz","optional":false},{"name":"C#","optional":true},{"name":"Kubernetes","optional":true}],"status":"live","first_seen_at":"2026-07-30T00:00:00Z","employer_posted_date":"2026-07-30","last_verified_at":"2026-10-14T06:31:15Z","board_verified":true,"closed_at":null,"days_open":76,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":76},"description":"Senior AI EngineerReq number:\nR8164Employment type:\nFull timeWorksite flexibility:\nHybridWho we are\nCAI is a global services firm with over 9,000 associates worldwide and a yearly revenue of $1.3 billion+. We have over 40 years of excellence in uniting talent and technology to power the possible for our clients, colleagues, and communities. As a privately held company, we have the freedom and focus to do what is right-whatever it takes. Our tailor-made solutions create lasting results across the public and commercial sectors, and we are trailblazers in bringing neurodiversity to the enterprise.\nJob Summary\nThe Senior AI Engineer is to design, build, integrate, and operate secure, scalable, and production-ready AI solutions within an enterprise environment.The role combines strong Python / .NET software engineering with generative AI engineering, AWS-native cloud architecture, enterprise system integration, observability, and application security. The Senior AI Engineer will provide technical leadership across the engineering lifecycle, from solution design and experimentation through implementation, production deployment, monitoring, and continuous improvement.\nJob Description\nWe are looking for Senior AI Engineer to design, build, integrate, and operate secure, scalable, and production-ready AI solutions within an enterprise environment. The role combines strong Python / .NET software engineering with generative AI engineering, AWS-native cloud architecture, enterprise system integration, observability, and application security. The Senior AI Engineer will provide technical leadership across the engineering lifecycle, from solution design and experimentation through implementation, production deployment, monitoring, and continuous improvement. This position will be full-time and hybrid at Mandaluyong City.\nWhat You'll Do\nTranslate business and product requirements into appropriate technical designs, implementation plans, and engineering tasks\n\nLead technical design reviews and contribute to architecture, security, data, and operational readiness assessments\n\nEvaluate technical options and provide recommendations based on feasibility, scalability, security, performance, cost, and maintainability\n\nIdentify technical dependencies, delivery risks, resource requirements, and architectural constraints early in the development lifecycle\n\nGuide engineers in resolving complex technical issues involving AI models, application services, data pipelines, cloud infrastructure, and enterprise integrations\n\nSupport technical estimation, work planning, backlog refinement, and delivery prioritization\n\nPromote the use of shared enterprise AI capabilities and reusable platform services rather than duplicating solution-specific implementations\n\nMentor junior and mid-level engineers through code reviews, design discussions, pair programming, and knowledge-sharing sessions\n\nBackend Software Engineering\n\nDesign and develop high-quality Python and/or .NET services, libraries, APIs, background workers, and data-processing components\n\nBuild modular, reusable, testable, and maintainable application components using established Python and/or .NET engineering practices\n\nDevelop synchronous and asynchronous services that support AI inference, document processing, data retrieval, workflow orchestration, and system integration\n\nImplement appropriate exception handling, retry mechanisms, timeouts, circuit breakers, caching, rate limiting, and graceful degradation\n\nApply object-oriented, functional, domain-driven, and event-driven design approaches where appropriate\n\nDevelop automated unit, integration, contract, security, performance, and regression tests\n\nMaintain clear technical documentation covering solution architecture, APIs, configuration, deployment, operations, and troubleshooting\n\nContribute to continuous integration and continuous delivery pipelines for automated testing, security scanning, deployment, and release management\n\nParticipate in code reviews and ensure that engineering work meets agreed quality, security, performance, and maintainability standards\n\nSystem Integrations\n\nDesign and implement secure integrations between AI solutions and enterprise applications, data platforms, document repositories, workflow systems, and external services\n\nDevelop and maintain REST, event-driven, messaging, streaming, batch, and file-based integration patterns\n\nBuild integrations using APIs, webhooks, message queues, event buses, managed file transfer, and other approved enterprise integration mechanisms\n\nImplement authentication and authorization using enterprise identity standards such as OAuth 2.0, OpenID Connect, service identities, API credentials, and role-based access controls\n\nIntegrate AI solutions with structured and unstructured data sources while preserving source permissions, data classifications, and access-control requirements\n\nDevelop connectors for enterprise systems such as document management platforms, service management tools, data warehouses, databases, search platforms, and business applications\n\nDefine API contracts, data schemas, error-handling conventions, versioning strategies, and integration testing requirements\n\nCoordinate with application owners and platform teams to resolve integration constraints, access requirements, service limits, and dependency timelines\n\nEnsure that integrations are observable, resilient, idempotent where necessary, and designed to handle partial failures safely\n\nAWS-Native Cloud Engineering\n\nDesign and implement AI solutions using approved AWS-native cloud services and architectural patterns\n\nDevelop solutions using relevant services such as Azure OpenAI, Amazon Bedrock, AWS Lambda, Amazon ECS, Amazon EKS, Amazon API Gateway, Amazon S3, Amazon RDS, Amazon OpenSearch Service, Amazon EventBridge, Amazon SQS, Amazon SNS, AWS Step Functions, and AWS Secrets Manager\n\nImplement cloud-native patterns for serverless processing, containerized workloads, event-driven architecture, workflow orchestration, batch processing, and API-based services\n\nDesign solutions that meet enterprise requirements for availability, scalability, resilience, performance, backup, disaster recovery, and cost management\n\nWork with cloud infrastructure teams to define network connectivity, private endpoints, security groups, encryption, logging, and environment configurations\n\nContribute to infrastructure-as-code implementations using Terraform\n\nOptimize cloud resource usage, model consumption, storage, data transfer, and compute costs\n\nSupport deployments across development, testing, staging, and production environments\n\nTroubleshoot application, platform, network, permissions, capacity, and service-integration issues within AWS environments\n\nGenerative AI Engineering\n\nDesign and build generative AI solutions using foundation models, large language models, embedding models, reranking models, and multimodal capabilities\n\nDevelop retrieval-augmented generation applications that combine enterprise content, search services, vector retrieval, metadata filtering, and generative models\n\nDesign prompt templates, system instructions, tool descriptions, response schemas, and conversation flows\n\nImplement model routing, fallback, retry, timeout, caching, and rate-limiting mechanisms\n\nBuild AI agent and workflow capabilities that can select approved tools, retrieve information, invoke enterprise services, and complete controlled multi-step tasks\n\nDevelop document ingestion, parsing, chunking, metadata enrichment, embedding, indexing, retrieval, and citation-generation pipelines\n\nImplement hybrid search approaches that may combine keyword search, semantic search, vector search, taxonomy, graph-based retrieval, and reranking\n\nWork with Data Scientists and AI Engineers to evaluate models and AI responses using measurable criteria such as relevance, groundedness, accuracy, completeness, safety, latency, and cost\n\nDevelop automated and human-in-the-loop evaluation processes for prompts, models, retrieval strategies, and generated outputs\n\nImplement safeguards against prompt injection, insecure tool invocation, sensitive-data exposure, hallucination, inappropriate content, and unauthorized information retrieval\n\nSupport AI red-teaming, adversarial testing, security testing, and responsible AI assessments\n\nMonitor model behaviour, token consumption, response quality, retrieval performance, latency, failures, and operational cost\n\nDocument model limitations, solution assumptions, evaluation results, human oversight requirements, and appropriate-use conditions\n\nCollaborate with product managers and business stakeholders to clarify requirements, intended outcomes, user expectations, and acceptance criteria\n\nWork with solution and enterprise architects to ensure alignment with organizational architecture principles and technology standards\n\nPartner with data engineers and data owners to address data quality, availability, lineage, classification, access, licensing, and refresh requirements\n\nCoordinate with infrastructure, platform engineering, and site reliability teams to establish production environments and operational support models\n\nWork closely with cybersecurity, data security, risk, legal, compliance, and responsible AI teams to implement required controls\n\nParticipate in agile ceremonies, technical workshops, architecture reviews, security reviews, and operational readiness assessments\n\nCommunicate technical risks, dependencies, design decisions, implementation trade-offs, and delivery progress clearly to technical and non-technical stakeholders\n\nSupport delivery partners and vendors by defining technical expectations, reviewing deliverables, and ensuring alignment with enterprise engineering standards\n\nContribute to engineering communities of practice, reusable technical assets, reference implementations, and internal knowledge repositories\n\nFoster a collaborative engineering culture that encourages constructive challenge, early escalation, shared ownership, and continuous improvement\n\nImplement application, infrastructure, model, retrieval, integration, and user-experience observability using Datadog\n\nConfigure structured logging, metrics, distributed tracing, dashboards, alerts, monitors, and service-level indicators\n\nInstrument Python services to provide visibility into request processing, dependencies, database activity, external API calls, model invocations, and background jobs\n\nEstablish correlation across application logs, traces, infrastructure telemetry, model requests, and business transactions\n\nMonitor AI-specific operational metrics such as model latency, token usage, inference failures, retrieval quality, empty results, fallback rates, and cost\n\nDefine meaningful alerts that support early detection while minimizing unnecessary operational noise\n\nSupport incident investigation, root-cause analysis, performance tuning, and post-incident improvement activities\n\nEnsure that logs and telemetry are designed to avoid exposing credentials, confidential data, personal information, prompts, or model responses without appropriate controls\n\nDevelop operational dashboards for engineering teams, service owners, product managers, and support functions\n\nContribute to service-level objectives, availability targets, performance baselines, and capacity planning\n\nEmbed secure software development practices throughout the design, development, testing, deployment, and operation of AI solutions\n\nUse Wiz to identify and address cloud security risks, exposed resources, configuration weaknesses, excessive permissions, vulnerable workloads, and security posture issues\n\nUse Snyk to identify and remediate vulnerabilities in open-source dependencies, application code, container images, and infrastructure-as-code\n\nIntegrate security scanning and policy checks into continuous integration and continuous delivery pipelines\n\nReview and remediate software composition analysis, static application security testing,...","description_format":"text","description_chars":18773,"description_truncated":true,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Continuous learning"],"hiring_locations":[{"name":"Philippines","iso":"PH","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Application Security"],"lifecycle":[{"event":"open","at":"2026-10-13T04:26:19Z"}],"visa":[],"liveness":{"score":16,"band":"cold","label":"Long shot","p_open":1,"p_active":0.446,"p_room":0.36,"age_days":76,"expected_fill_days":39,"reasons":["conf:0","stale_co","win:tail","crowd:brand"],"computed_at":"2026-10-14T06:34:16Z"},"pay":null,"html_url":"https://alion.io/job/cai-senior-ai-engineer","json_url":"https://alion.io/job/cai-senior-ai-engineer.json","meta":{"generated_at":"2026-10-14T08:12:18Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"assistant_verified","counted_by":"address","units_charged":1,"used_today":1428,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-15T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":1819291},"rest":"https://alion.io/mcp/rest/get_company?id=1819291"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fcai-senior-ai-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fcai-senior-ai-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fcai-senior-ai-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/cai-senior-ai-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fcai-senior-ai-engineer"}]}