BlitzenX is looking for an AI Architect who can build AI capabilities from zero to scale.

This role is not about experimentation or research for vanity metrics.

This is about designing, building, and shipping real AI products that drive business outcomes.

You will define the AI vision, build and lead high-performance AI teams, and own the product roadmap from concept to production.

You will work closely with executive leadership, product managers, and engineering heads to turn AI strategy into deployed, revenue-impacting solutions.

This role demands technical authority, architectural depth, leadership maturity, and delivery discipline.

Key Responsibilities :

AI Architecture & Platform Ownership :

- Define and own the end-to-end AI architecture across data ingestion, model development, training, deployment, monitoring, and continuous improvement.

- Design scalable, secure, and cost-efficient AI platforms leveraging cloud, MLOps, and modern data architectures.

- Establish reference architectures, design standards, and technical guardrails for all AI initiatives.

- Ensure AI systems are production-grade, not experimental prototypes.

Product Roadmap From Scratch to Delivery :

- Partner with business and product leaders to identify AI use cases aligned to revenue, efficiency, or customer impact.

- Translate ambiguous business problems into clear AI product roadmaps with milestones, risks, and measurable outcomes.

Own The Roadmap Across :

- Use-case discovery.

- Data strategy.

- Model selection and build.

- MVP definition.

- Production rollout.

- Iterative scaling and optimization.

- Drive on-time, predictable delivery of AI products with clear success metrics.

Team Building & Leadership :

- Build AI teams from the ground up data scientists, ML engineers, platform engineers, and AI product contributors.

- Hire for execution strength, not academic theory.

- Define team structure, roles, and ownership models that scale.

- Establish a high-accountability, high-output culture with clear performance expectations.

- Mentor senior engineers and architects to raise the overall technical bar.

Execution & Governance :

- Set up AI development lifecycle processes (MLOps, CI/CD, model governance, monitoring).

Define Standards For :

- Model versioning.

- Explainability.

- Bias detection.

- Security and compliance.

- Act as the final technical authority on AI decisions.

- Balance speed with stability - no uncontrolled experimentation in production.

Stakeholder & Executive Engagement :

- Communicate AI strategy, architecture, and progress clearly to senior leadership and clients.

- Influence decision-making with data, clarity, and technical credibility.

- Support pre-sales and solutioning efforts where AI is a differentiator.

Required Qualifications :

- 10+ years of overall experience with 5+ years in AI/ML architecture or leadership roles.

- Proven experience building AI products from zeronot inheriting mature systems.

Strong Hands-on Background In :

- Machine Learning & Deep Learning.

- NLP, Computer Vision, or Predictive Modeling (at least one deeply).

- Deep expertise in cloud platforms (AWS / Azure / GCP) for AI workloads.

- Strong understanding of MLOps frameworks, model deployment, and monitoring.

- Experience leading and scaling cross-functional AI teams.

- Ability to make architectural decisions under ambiguity and pressure.

Technical Expectations :

- Solid programming background (Python mandatory).

- Experience with modern ML frameworks (TensorFlow, PyTorch, etc.

- Strong data architecture fundamentals (data lakes, feature stores, pipelines).

- Familiarity with APIs, microservices, and production system integration.

- Clear understanding of performance, cost optimization, and scalability trade-offs.

What Success Looks Like :

- AI teams are built, stable, and delivering consistently.

- AI product roadmaps move from idea to production without chaos.

- Models are deployed, monitored, and improvednot abandoned.

- Leadership trusts your technical decisions.

- AI is no longer a buzzword inside BlitzenXit is a reliable product capability.

Mindset Fit :

- Builder, not theorist.

- Comfortable with ownership and accountability.

- Execution-first mentality.

- High standards for engineering quality and delivery discipline.

- Thrives in a performance-driven, employee-first culture.

If You Want, Next I Can :

- Tighten this further for LinkedIn posting.

- Customize it for GenAI / LLM-focused architecture.

- Align it specifically to client-facing AI platforms vs internal products.

- Convert it into a BlitzenX hiring scorecard.

Just tell me the direction.