Principal Architect - Data Science & AI

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