Artificial Intelligence Architect

BlitzenX; Chennai District; On-site; INR 2,500,000 - 3,500,000; Full time

Job summary

BlitzenX is seeking an AI Architect to create and manage AI capabilities from ground up. This position involves defining AI architecture, building high-performance teams, and ensuring the delivery of impactful AI products. The ideal candidate should have extensive experience and a strong technical foundation in AI methodologies.

Key responsibilities include collaborating with leadership on AI strategy, establishing architecture standards, and overseeing the development lifecycle. This role is crucial for driving business outcomes through effective AI solutions.

Qualifications

• 10+ years of overall experience with 5+ years in AI/ML architecture or leadership roles.
• Proven experience building AI products from zero.
• Strong understanding of MLOps frameworks, model deployment, and monitoring.

Responsibilities

• Define and own the end-to-end AI architecture.
• Design scalable, secure, and cost-efficient AI platforms.
• Build AI teams from the ground up.

Skills

Machine Learning & Deep Learning

NLP

Computer Vision

Predictive Modeling

Machine Learning frameworks (TensorFlow, PyTorch)

Programming in Python

Cloud platforms (AWS / Azure / GCP)

MLOps frameworks

Tools

APIs

Microservices

Job description

Role Summary

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.

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

Own The Roadmap Across

• Use-case discovery
• Data strategy
• Model selection and build
• MVP definition
• Production rollout
• Iterative scaling and optimization

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 zero — not 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 improved — not abandoned.
• Leadership trusts your technical decisions.
• AI is no longer a buzzword inside BlitzenX—it 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.