Enterprise Architect (Data & AI)

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

1. 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

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

• Lead 4–6 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 demo‐to‐close rate)

1. 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)

1. 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

1. 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 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 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