Enterprise Architect

As an Enterprise Architect, you will own the end-to-end technology blueprint, spanning backend platforms (Java/.NET, Python), frontend frameworks (React, Angular, ), 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

1. Architecture & AI Roadmap

2. Deliver a three-year, multi-domain blueprint covering cloud, data, integration, AI/ML, and agentic-AI agents

3. Stand up an AI & Agentic Architecture Council (quarterly) driving adoption of generative AI, conversational agents, and MLOps standards

4. AI-First Proof-of-Concepts & Agentic Demos

5. Lead 4–6 POCs/year around AI/ML and agentic use cases (e.g., LLM-powered assistants, workflow orchestration bots)

6. Measure POC success by model accuracy (+15% lift), inference latency (2× faster), and business KPIs (reduced support tickets, increased demo‐to‐close rate)

7. Team Enablement & AI Mentorship

8. 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

9. Embed AI/agentic design patterns into standard playbooks (prompt engineering, feedback loops, multi-agent coordination)

10. GTM & Presales Enablement

11. Collaborate with Sales Engineering to craft technical demos, solution blueprints, and ROI analyses for enterprise prospects

12. Support bid responses and RFPs with architecture diagrams, security/compliance narratives, and scalability proof points

13. Resilience & Responsible AI

14. Define and track system and model health metrics (system uptime ≥99.9%; model drift ≤5% per quarter)

15. 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 policies—data 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 ; backend APIs with 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, , ,
• 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