Tech S and T-Cloud AI Platform Architect-ISR-Manager-GDSF02
About the job
- A B.Tech or B.E. degree in Computer Science, Information Technology, Artificial Intelligence, or Machine Learning is required.
- Hands-on experience designing and managing enterprise AI platforms, model hosting environments, inference systems, vector database infrastructure, API-based AI services, and secure runtime environments is required.
- Deep expertise in AWS, Azure, and Google Cloud Platform, including SageMaker, Bedrock, Azure Machine Learning, Azure OpenAI, Vertex AI, AKS, EKS, GKE, identity and access management, networking, storage, and monitoring, is required.
- Strong knowledge of Terraform, Bicep, ARM templates, CloudFormation, continuous integration and continuous delivery pipelines, containerization, Kubernetes, deployment automation, microservices architecture, and release engineering is required.
- Experience with MLflow, Kubeflow, Azure Machine Learning pipelines, Vertex AI pipelines, model registries, experiment tracking, model serving, deployment governance, and monitoring is required.
- Understanding of ETL and ELT pipelines, Airflow, Prefect, Spark, Kafka, Databricks, feature stores, streaming and batch processing, and production data pipelines for AI workloads is required.
- Proficiency in Python, shell scripting, YAML, JSON, REST APIs, FastAPI, and automation scripting for platform and cloud operations is required.
- Familiarity with platform security, secrets management, policy controls, auditability, observability, Responsible AI, and enterprise governance requirements is required.
- Ten to thirteen years of experience in cloud platform engineering, AI platform engineering, DevOps, MLOps, or AI consulting is required.
- A proven track record leading enterprise AI platform implementations across BFSI, manufacturing, healthcare, or the public sector is required.
- Experience with sovereign AI, regulated-industry deployments, and multi-cloud architecture programs is required.
- Hands-on experience building production-grade cloud AI platforms, automated deployment frameworks, MLOps pipelines, and enterprise AI infrastructure is mandatory for the interview stage.
- Lead the design and implementation of enterprise AI platforms supporting AI/ML, generative AI, agentic AI, and retrieval-augmented generation workloads across hybrid and multi-cloud environments.
- Architect scalable cloud-native AI foundations using AWS SageMaker and Bedrock, Azure Machine Learning and Azure OpenAI, Google Cloud Vertex AI, and enterprise Kubernetes platforms such as EKS, AKS, and GKE.
- Build secure and resilient infrastructure for AI model development, training, deployment, and runtime operations, including compute, GPU environments, storage, networking, secrets management, and access control.
- Design reusable platform services for model hosting, inference endpoints, vector databases, prompt orchestration, agent runtime support, and enterprise API integration.
- Establish platform observability with centralized logging, monitoring, tracing, telemetry, performance diagnostics, and cost optimization for AI systems.
- Enable secure AI platform controls through policy enforcement, access governance, auditability, Responsible AI, compliance, and risk support.
- Drive standardization of AI platform architecture through reusable patterns, landing zones, environment templates, and enterprise engineering best practices.
- Serve as the primary technical advisor to CxOs, account leadership teams, and enterprise engineering stakeholders on AI platform strategy, cloud modernization, and deployment architecture.
- Conduct technical workshops demonstrating platform blueprints, deployment models, MLOps capabilities, observability approaches, and AI operational readiness.
- Translate complex infrastructure capabilities into scalable business outcomes and delivery roadmaps.
- Build trusted client relationships through hands-on proof-of-concept delivery, architecture discussions, and strategic guidance on AI platform adoption.
- Lead automation of AI infrastructure provisioning using Infrastructure as Code tools such as Terraform, Bicep, ARM templates, and CloudFormation.
- Design and implement continuous integration, continuous delivery, and continuous training pipelines for AI platforms, model deployment services, APIs, microservices, and environment lifecycle management.
- Drive containerization and orchestration patterns using Docker and Kubernetes, including workload isolation, autoscaling, high availability, and release automation.
- Build deployment frameworks for AI and generative AI applications using REST services, FastAPI, model-serving frameworks, serverless patterns, and integration middleware.
- Automate configuration management, secrets handling, policy validation, artifact promotion, and environment provisioning across development, test, and production environments.
- Implement release engineering practices including blue-green deployment, canary rollout, rollback mechanisms, and platform upgrade planning.
- Ensure platform reliability, scalability, and supportability through automated testing, operational runbooks, resilience engineering, and incident readiness.
- Lead the setup and scaling of MLOps and LLMOps capabilities including experiment tracking, model versioning, model registries, artifact management, pipeline orchestration, and deployment governance.
- Establish standardized operationalization pipelines for model training, validation, packaging, deployment, monitoring, and retraining across enterprise AI use cases.
- Integrate AI platforms with batch and real-time data pipelines using Airflow, Prefect, Spark, Kafka, Databricks, and cloud-native data services.
- Design and govern reliable data flows for AI workloads including ingestion, transformation, feature engineering, feature store integration, vector store enablement, and metadata lineage.
- Implement monitoring for models and services covering drift detection, latency, throughput, failure diagnostics, and service-level performance.
- Collaborate with data science, AI engineering, and application teams to productionize prototypes, notebooks, and generative AI use cases into enterprise-grade deployments.
- Drive operational best practices around reproducibility, traceability, auditability, and regulated or risk-sensitive environment support.
- Lead creation of EY intellectual property including AI platform reference architectures, reusable Infrastructure as Code modules, deployment blueprints, MLOps templates, and observability accelerators.
- Develop reusable frameworks for secure AI environment setup, platform monitoring dashboards, model deployment factories, and retrieval-augmented generation infrastructure enablement.
- Package technical accelerators as EY market offerings for rapid client deployment across industries and cloud ecosystems.
- Lead technical solutioning for AI platform transformation RFPs, including architecture blueprints, deployment approaches, cloud landing patterns, and live proof-of-concept demonstrations.
- Develop industry-specific go-to-market strategies combining EY assets with hyperscaler AI platform services and enterprise automation capabilities.
- Collaborate with sales teams to position EY as a trusted partner for AI platform engineering, cloud AI operationalization, and secure enterprise AI deployment.
- Oversee end-to-end AI platform transformation programs from infrastructure discovery through production deployment, operational handover, and value realization.
- Manage cross-functional delivery teams including cloud engineers, platform engineers, machine learning engineers, DevOps specialists, and data engineers across multiple workstreams.
- Ensure technical excellence, risk mitigation, and key performance indicator achievement in complex enterprise AI platform and deployment programs.
- Mentor consultants and senior staff in AI platform engineering, cloud architecture, automation practices, and MLOps operationalization across the broader AI practice.
- Lead technical recruitment and capability building for cloud AI platform engineering excellence.
- Drive innovation proof-of-concept initiatives showcasing enterprise AI platforms, scalable generative AI deployment models, advanced observability, and sovereign AI infrastructure capabilities.
- An M.Tech or Master of Science degree in Cloud Computing, Artificial Intelligence, Data Engineering, or Machine Learning is preferred.
- Microsoft Certified: Azure AI Engineer Associate, Azure DevOps Engineer, or Azure Solutions Architect certification is desirable.
- AWS Certified Machine Learning Specialty, AWS DevOps Engineer, or AWS Solutions Architect certification is desirable.
- Google Professional Machine Learning Engineer or Professional Cloud DevOps Engineer certification is desirable.
- Kubernetes certifications such as CKA or CKAD, Terraform Associate, Databricks certifications, and MLOps or cloud platform engineering certifications are desirable.
- Big Four or technology consulting experience is preferred.
- Exceptional technical storytelling for CxO audiences is desirable.
- The ability to influence senior stakeholders through working prototypes, architecture deep dives, and platform transformation roadmaps is desirable.
- Leadership of diverse technical teams with a clear delivery focus is desirable.
About the company
EY (Ernst & Young) provides professional services at a global scale, offering consulting, assurance, tax, and transaction advisory services. It serves clients across industries such as technology, media, real estate, hospitality, and construction. Instead of selling a single product, EY works with clients through tailored engagements where cross-disciplinary teams analyze challenges, design strategies, perform audits, help with tax planning, and assist with mergers or divestitures. What sets EY apart is its worldwide reach and integrated service model, industry-specific expertise, and focus on responsible business practices like sustainability, cybersecurity, and workforce flexibility. EY’s goal is to help organizations improve performance, manage risk, and achieve sustainable growth while building a better working world.
Company Size
5,001-10,000
Company Stage
N/A
Total Funding
N/A
Headquarters
Boston, Massachusetts
Founded
1991
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Simplify's Take
What believers are saying
- EY.ai Agentic for Sales launched March 2, 2026 with Snowflake and Canva.
- EY uses client-zero deployments, scaling Copilot across 400,000 people by May 2026.
- EY’s 2026 AI assurance push monetizes client demand for governance, controls, and risk reviews.
What critics are saying
- Sabah filed RM2.4 billion audit-negligence claims against EY on July 30, 2026.
- EY set aside £188 million in March 2026 after NMC, Shell, and Made.com scrutiny.
- Repeated audit failures can destroy trust, trigger client defections, and shrink the franchise permanently.
What makes Ernst & Young unique
- EY Canvas processes 1.4 trillion journal-entry lines across 160,000 global audits.
- EY-Microsoft invested US$1b+ in May 2026 to industrialize enterprise AI delivery.
- EY launched agentic audit AI on April 7, 2026, spanning Assurance and AI assurance.
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Benefits
Professional Development Budget
Flexible Work Hours
Remote Work Options
Company News
EY added nearly 10,000 staff globally in its 2026 financial year as AI-related revenue surged almost 50%. The Big Four firm's workforce reached 415,000, with most growth concentrated in global delivery services and offshoring centres. The Americas division saw headcount decrease by roughly 4,500, or 5%. Overall global revenue rose 4.7% to $57 billion. EY-Parthenon was the fastest-growing service line, with revenue increasing 7.4% to $6.8 billion, marking a turnaround after two years of minimal growth. Tax revenue rose 6% to $13.8 billion, whilst consulting grew 4.4% to $17.4 billion. The results reflect EY's "All-In" strategy under global chair Janet Truncale, with billions invested in transforming operations and AI services.
An Ernst & Young study found US oil and gas producers reached record production in 2025 while becoming more selective with investments. The analysis of 30 major exploration and production companies, representing 43% of US oil and gas output, showed oil production hit a study-period high, yet reserve additions from extensions and discoveries declined 11% year over year and failed to fully replace production volumes for the first time since 2021. Total capital expenditures fell 49% year over year, whilst M&A spending declined 70%. Exploration spending dropped 11% to $4.8 billion, representing only 3% of total capital expenditures. Natural gas showed stronger fundamentals, with production rising 18%, reserves increasing 14%, and discoveries up 21%.
Ernst & Young's US division is allocating $100 million this fiscal year to bonus payments rewarding employees who demonstrate adaptability, innovation, and judgement. Individual spot awards reach $500, whilst employees or teams making significant contributions can receive between $10,000 and $25,000, five times the previous programme's ceiling. The initiative also covers AI experimentation. "What we recognise signals what we value," said Ginnie Carlier, chief talent and culture officer for EY Americas. The bonuses form part of a broader strategy to reshape employee development across all career levels. Other professional services firms are pursuing similar approaches. KPMG restructured its audit internship this summer to emphasise critical thinking, whilst PwC US introduced training combining AI proficiency with human qualities like empathy. EY reported AI-related revenue grew 30% year-over-year in 2025.
EY-Parthenon has acquired SparkOptimus, a Dutch digital strategy consultancy founded in 2010. The Amsterdam-based firm employs around 50 consultants and specialises in AI transformation, digital business model redesign and technology-driven transformation. SparkOptimus founders Alexandra Jankovich and Tom Voskes, both former McKinsey consultants, said joining EY-Parthenon will create significant value for clients and staff whilst providing access to broader capabilities. The acquisition marks EY-Parthenon's first European deal since 2021. EY-Parthenon, established in 2014, is EY's strategy consulting and transactions advisory business with around 25,000 professionals globally. Mark Reich, Partner at EY-Parthenon Netherlands, said SparkOptimus' expertise will complement existing capabilities in the Dutch market. The deal closes on 1 September 2026. Financial terms were not disclosed.
The UK Government has awarded a contract worth up to £456 million to KPMG and EY to train civil servants, the Financial Times reported, citing government procurement tracker Tussell. Under the arrangement, the firms will train officials across various skills areas, including AI, between 2026 and 2028. KPMG's share is capped at £319 million, representing almost a quarter of its total UK advisory net sales from last year. EY's portion is worth £137 million, equivalent to around 13% of its UK consulting revenue. The deal is the largest single contract awarded to Big Four companies since Tussell started tracking records in 2012. The previous record was a £322 million deal between the Foreign Office and PricewaterhouseCoopers in 2012.