As an Enterprise Architect, you will own the end-to-end technology blueprint, spanning backend platforms (Java/.NET, Python), frontend frameworks (React, Angular, Node.js), real-time data streaming, and AI-driven/agentic services. You will translate business objectives into an actionable, multi-year technology and AI roadmap; ensure that every layer (application, data, infrastructure, security, AI, agentic agents) is aligned and future-proof; and act as the bridge between C-suite strategy, product, sales engineering (presales), and delivery teams.
Key Deliverables & Success Metrics
- Architecture & AI Roadmap
- Deliver a three-year, multi-domain blueprint covering cloud, data, integration, AI/ML, and agentic-AI agents
- Stand up an AI & Agentic Architecture Council (quarterly) driving adoption of generative AI, conversational agents, and MLOps standards
- AI-First Proof-of-Concepts & Agentic Demos
- Lead 46 POCs/year around AI/ML and agentic use cases (e.g., LLM-powered assistants, workflow orchestration bots)
- Measure POC success by model accuracy (+15% lift), inference latency (2 faster), and business KPIs (reduced support tickets, increased demotoclose rate)
- Team Enablement & AI Mentorship
- Launch a monthly AI & Agentic Deep Dive series to upskill engineers, data scientists, and presales consultants on ML frameworks (TensorFlow, PyTorch), conversational-AI patterns, and agent orchestration
- Embed AI/agentic design patterns into standard playbooks (prompt engineering, feedback loops, multi-agent coordination)
- GTM & Presales Enablement
- Collaborate with Sales Engineering to craft technical demos, solution blueprints, and ROI analyses for enterprise prospects
- Support bid responses and RFPs with architecture diagrams, security/compliance narratives, and scalability proof points
- Resilience & Responsible AI
- Define and track system and model health metrics (system uptime 99.9%; model drift 5% per quarter)
- Lead AI fairness & ethics reviews, ensuring bias detection, explainability, and compliance with GDPR/ADA
Extended Responsibilities:
A. Strategic Architecture & Agentic-AI Planning
- Enterprise Blueprint: Evolve the canonical reference architecture to include AI/ML pipelines, feature stores, inference-at-the-edge, and autonomous agent orchestration
- Cloud & Hybrid AI: Architect cloud-native AI/agentic services (SageMaker, Azure ML, Vertex AI Agents), hybrid inference runtimes, and GPU/TPU provisioning strategies
- Standards & Policies: Author AI governance policiesdata privacy, model validation, versioning, rollback strategies, and agent safety guardrails
B. Solution & AI-Driven Design
- Core Platforms: Architect mission-critical microservices on Java/Spring Boot, .NET Core, and Python (Django, Flask, FastAPI) with embedded AI inference and agentic endpoints (REST/GRPC)
- Frontend & Full-Stack: Design rich client applications using React, Angular, or Vue.js; backend APIs with Node.js/Express or Python frameworks; implement CI/CD for full-stack deployments
- Data & Streaming: Design streaming ETL with Kafka + Spark/Flink feeding feature stores, real-time scoring engines, and agent event buses
- MLOps & AI Ops: Define CI/CD for models (training, validation, deployment), automated retraining triggers, canary and shadow deployments, plus agent lifecycle management
C. Governance & Responsible AI
- Architecture Reviews: Include an ML & agentic risk dimension in every design review (performance, security, bias, unintended behaviors)
- Security & Compliance: Partner with InfoSec to secure code, model artifacts, and agent logic (encryption, access controls, audit trails); vet third-party AI/agentic services
- FinOps for AI: Implement cost-optimization for GPU/compute, track ROI on AI and agentic initiatives (cost per model endpoint, agent-handling cost per transaction)
D. Leadership, GTM & Collaboration
- Cross-Functional Engagement: Work closely with Product, UX, Sales Engineering, and Security to define AI/use-case roadmaps, demo strategies, and success criteria
- Presales Coaching: Mentor Solutions Architects and Sales Engineers on technical storytelling, POC/demo best practices, and objection handling around AI and agentic capabilities
- Community Building: Sponsor internal hackathons, open-source contributions (e.g., agent frameworks such as AutoGen, LangChain), and external speaking opportunities
E. AI & Agentic POC, Innovation, and GTM
- Rapid Experimentation: Prototype generative AI agents, semantic search with vector databases, autonomous workflow bots, and conversational-AI pipelines
- Benchmarking & Optimization: Lead performance profiling (JVM/CLR/Python interpreters), model quantization, optimization for CPU-only edge deployments, and low-latency agent responses
- GTM Support: Develop presales playbooks, ROI calculators, and competitive battlecards for AI and agent-driven offerings
Requirements:
- Bachelor's or Master's degree in Computer Science, Engineering, or related field
- 15+ years delivering enterprise-grade solutions with significant AI/ML and agentic-AI components
- Certifications (highly desirable): TOGAF 9.2, AWS Solutions Architect Professional, Azure Solutions Architect Expert, Certified Kubernetes Administrator (CKA), TensorFlow Developer Certificate
Mandatory Skills & Expertise
- Languages & Frameworks:
- Backend: Java (JEE, Spring Boot), .NET Core/Framework, Python (Django, Flask, FastAPI)
- Frontend & Full-Stack: React, Angular, Vue.js, Node.js/Express, Next.js/Nuxt.js
- APIs & Microservices: REST, gRPC, GraphQL, serverless functions (AWS Lambda, Azure Functions)
- Streaming & Real-Time Data: Apache Kafka (Streams, Connect), Pulsar, Spark/Flink, event sourcing/CQRS
- Cloud & AI Platforms: AWS (SageMaker, Lambda, ECS/EKS), Azure (ML, Functions, AKS), GCP (Vertex AI, Cloud Functions), Terraform, CloudFormation, Azure ARM
- Containers & Orchestration: Docker, Kubernetes (EKS/AKS/GKE), Helm, service meshes (Istio, Linkerd)
- Data Engineering & Feature Stores: Spark, Flink, Kinesis, S3/HDFS; data warehousing (Redshift, BigQuery, Snowflake); feature stores (Feast, Tecton)
- AI/ML & Agentic Lifecycle: TensorFlow, PyTorch, MLflow, Kubeflow, SageMaker Pipelines; conversational-AI frameworks (Rasa, Bot Framework); agentic frameworks (LangChain, AutoGen)
- Responsible AI & Ethics: Bias detection, explainability (SHAP, LIME), privacy-preserving ML (DP, federated learning), GDPR/PCI-DSS fundamentals
- Distributed Systems & Performance: CAP theorem, consensus (Raft/Paxos), JVM/CLR/Python tuning, algorithmic complexity analysis, network diagnostics
- GTM & Presales: Hands-on experience with technical presales, RFP/RFI responses, demo/PITCH deck creation, ROI analysis, competitive positioning
- Leadership & Collaboration: Architecture governance, technical mentorship, stakeholder management, workshop facilitation, cross-functional team leadership.
Preferred Attributes:
- Domain expertise in regulated industries (finance, healthcare, telecommunications)
- Active open-source contributions to AI/agentic or frontend/backend frameworks
- Proven track record driving agile transformations, DevSecOps, and responsible AI adoption at scale
