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

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 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)

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 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