Advisor / Senior Advisor - Technical Architect

enGen Global; Hyderabad, Chennai; On-site; INR 3,500,000 - 6,000,000; Full time

Job summary

enGen Global in Hyderabad seeks an accomplished Technical Architect to define scalable, secure, and cost-conscious technology solutions across a broad enterprise landscape.

You will lead architecture across GCP, mainframe, AI/ML, data, APIs, and platform engineering, translating business outcomes into target-state designs and guiding teams through modernization, governance, and risk management.

Qualifications

• Requires extensive hands-on experience in GCP, mainframe, and cloud modernization.
• Designs scalable, secure architectures with cost-conscious decisions.
• Proficient in Python/Java services and GraphQL/REST APIs.
• Expert in data architectures spanning relational, NoSQL, and graph databases.
• Experience with containerized deployments and DevSecOps practices.

Responsibilities

• Define current-state, transition-state, and target-state architectures aligned with business strategy and security requirements.
• Lead solution architecture across GCP cloud services, mainframe and distributed platforms, data, and AI capabilities.
• Evaluate architecture options and document trade-offs involving scalability, security, cost, and risk.
• Design cloud-native and hybrid solutions with compute, storage, networking, and identity patterns.
• Guide modernization of legacy workloads through rehosting, replatforming, and API enablement.

Skills

GCP cloud architecture

Mainframe modernization

Python development

Java development

GraphQL API design

Data architecture

AI/ML architecture

Container orchestration

Tools

Docker

Kubernetes

GKE

CI/CD

Job description

Job Description

Job Title: Advisor / Senior Advisor - Technical Architect - Advanced Technologies

Primary Technology: Cross-Technology Architecture: GCP Cloud, Mainframe, Application Engineering, Data, AI/ML, APIs and Platform Engineering

Role Overview

We are seeking an accomplished Technical Architect to define and govern scalable, secure, resilient, and cost-conscious technology solutions across a broad enterprise landscape. The role requires advanced, hands-on architecture experience spanning Google Cloud Platform (GCP), mainframe and cloud modernization, Python and Java application ecosystems, relational, NoSQL, distributed, and graph data platforms, GraphQL API architecture, data engineering, AI and machine learning, API and integration architecture, and container orchestration. The architect will translate business outcomes into target-state architectures, guide engineering teams through implementation, establish reusable standards and reference patterns, and manage technical risk across complex transformation programs.

Key Responsibilities

• Define current-state, transition-state, and target-state architectures aligned with business strategy, enterprise standards, security requirements, and measurable non-functional requirements.
• Lead solution architecture across GCP cloud services, mainframe and distributed platforms, applications, data, integrations, AI/ML capabilities, and platform engineering.
• Evaluate architecture options and document trade-offs involving scalability, availability, performance, security, operability, maintainability, cost, and delivery risk.
• Design cloud-native and hybrid solutions using appropriate compute, storage, networking, identity, observability, resilience, backup, and disaster-recovery patterns.
• Guide modernization of legacy and mainframe workloads through rehosting, replatforming, refactoring, API enablement, event-driven integration, and incremental decomposition where appropriate.
• Provide architecture direction for Python and Java services, GraphQL and REST API layers, asynchronous messaging, integration platforms, and reusable service contracts.
• Define data architecture patterns across relational, NoSQL, distributed, graph, analytical, and vector-capable platforms, including Google Cloud Spanner, Spanner Graph, SQL, and ISO GQL-compatible graph querying.
• Guide enterprise data engineering solutions covering batch and streaming ingestion, transformation, orchestration, metadata, lineage, data quality, goverce, and consumption layers.
• Shape responsible AI/ML solution architecture, including model integration, MLOps, data controls, monitoring, explainability, security, and operational support considerations.
• Establish container and orchestration standards using Docker and Kubernetes, including deployment, autoscaling, service networking, secrets, policy enforcement, and observability.
• Create architecture artifacts such as context diagrams, logical and deployment views, integration flows, data flows, architecture decision records, standards, and implementation roadmaps.
• Lead technical design reviews, architecture goverce, proofs of concept, risk assessments, and production-readiness reviews; ensure decisions are traceable and implementable.
• Partner with product, business, security, infrastructure, data, engineering, testing, operations, vendors, and leadership teams to drive outcomes across the delivery lifecycle.
• Mentor architects and senior engineers, resolve cross-team technical dependencies, and provide escalation support for complex design and production issues.

Mandatory Technical Skills

• Enterprise Architecture Architecture principles, reference architectures, capability mapping, roadmaps, architecture decision records, goverce Demonstrated ability to convert business strategy into pragmatic target-state and transition architectures and govern implementation across multiple teams.
• Google Cloud Platform GCP compute, networking, storage, IAM, observability, integration, data and AI services Advanced architecture experience designing secure, scalable, resilient, supportable, and cost-conscious workloads on GCP and in hybrid environments.
• Mainframe Modernization IBM z/OS, COBOL, JCL, DB2 for z/OS, CICS, IMS, VSAM, IBM MQ, batch and online processing, TSO/ISPF, SDSF, RACF concepts, workload assessment, API enablement, event-driven integration, CI/CD and modernization patterns Advanced architecture knowledge of mainframe application and data landscapes, batch and transaction processing, security, integration, performance, availability, disaster recovery, and phased modernization through rehosting, replatforming, refactoring, API enablement, event streaming, or selective decomposition without disrupting critical processing.
• Application Engineering Python, Java, microservices, domain-driven design, distributed systems, asynchronous processing Strong understanding of application architecture, service boundaries, concurrency, reliability, error handling, testing, and production operability.
• API Integration REST APIs, GraphQL APIs, API gateways and management, event-driven architecture, messaging, schema and contract goverce, service integration patterns Able to design reusable and secure API and integration solutions, including GraphQL service schemas and resolvers; manage authentication, authorization, versioning, idempotency, throttling, error handling, observability, and lifecycle goverce.
• Database Architecture Relational databases, NoSQL databases, Google Cloud Spanner, Spanner Graph, ISO Graph Query Language (GQL), graph databases, distributed data stores, vector-capable databases Broad database architecture knowledge with the ability to select fit-for-purpose platforms based on consistency, scale, latency, query patterns, transactions, relationships, and operational needs. Includes Spanner Graph for interoperable relational and property-graph models using SQL and ISO GQL-compatible graph queries.
• Data Engineering Batch and streaming pipelines, ETL/ELT, orchestration, data lakes/lakehouses, metadata, lineage, data quality Able to define governed, scalable data platforms and data-product patterns supporting operational, analytical, and AI use cases.
• AI Machine Learning AI/ML solution patterns, generative AI integration, MLOps, model serving, monitoring, responsible AI controls Architecture experience integrating AI capabilities into enterprise solutions with appropriate security, goverce, reliability, and lifecycle controls.
• Containers Orchestration Docker, Kubernetes, Google Kubernetes Engine (GKE), service networking, autoscaling, policy and secrets management Strong understanding of container platforms, workload deployment, resilience, observability, platform security, and operational standards.
• Security Resilience Zero Trust principles, identity and access management, encryption, secrets, threat modeling, availability, DR Embeds security, privacy, compliance, reliability, recovery, and operational readiness into architecture decisions from inception.
• DevSecOps Observability CI/CD, infrastructure as code, automated quality gates, logging, metrics, tracing, SRE practices Defines delivery and operational patterns that enable repeatable releases, measurable service health, rapid diagnosis, and controlled change.
• Architecture Communication Executive communication, technical storytelling, diagrams, standards, design reviews, stakeholder facilitation Communicates complex decisions clearly to engineering teams and senior stakeholders, with explicit assumptions, trade-offs, risks, and recommendations.