Project Role : AI Infrastructure Architect

Project Role Description : Architect and build custom Artificial Intelligence (AI) infrastructure/hardware solutions. Optimize AI infrastructure/hardware performance, power consumption, cost and scalability of computational stack. Advise on AI infrastructure technology and vendor evaluation, selection and full stack integration.

Must have skills : Databricks Unified Data Analytics Platform

Good to have skills : AI Agents & Workflow Integration

Minimum 15 Year(s) Of Experience Is Required

Educational Qualification : 15 years full time education

Role Summary / Description

AI Powered Tech Talent

As an Technical Architect in AI Infrastructure Architecture, you will act as a senior technical authority for Databricks-based AI/ML and lakehouse infrastructure, shaping the technical vision, reference architecture, standards and implementation strategy for large-scale AI systems. You will evaluate complex choices across workspace architecture, compute clusters, model lifecycle, model serving, data/feature pipelines, governance, observability, security and cost optimization, while guiding senior and lead architects/Technical Architects to deliver resilient, scalable and production-ready AI infrastructure. You will bring industry experience across enterprise AI adoption, compliance, reliability, FinOps and platform modernization to help clients translate AI infrastructure trade-offs into measurable business value.

Key Responsibilities

Set the overarching Databricks AI infrastructure vision, strategy and reference architecture for large-scale AI/ML and lakehouse systems, including workspace architecture, compute, storage, orchestration, model serving and observability.

Own complex architectural decisions across Databricks workspaces, clusters/serverless compute, jobs, MLflow, Model Registry, Unity Catalog, Feature Technical Architecting, Delta Lake and cloud integrations, rationalizing options against client standards and business objectives.

Architect and prototype cost-optimized distributed training, feature Technical Architecting and model-serving environments, building benchmarks, proof-of-concepts and reusable implementation patterns.

Define architecture standards, reusable infrastructure-as-code patterns, CI/CD approaches, ML pipeline deployment patterns, monitoring strategy, SLAs/SLOs and cost/performance governance for production AI/ML systems.

Lead architecture assessments and design reviews validate findings through hands-on implementation, profiling, performance tuning and troubleshooting across jobs, clusters, libraries, storage, security and serving layers.

Evaluate emerging Databricks, lakehouse, vector search, LLMOps and model-serving capabilities, and recommend where they belong in enterprise solutions.

Provide executive and client-level technical advisory, translating platform trade-offs into clear, defensible recommendations connected to business outcomes.

Mentor architects and Technical Architects, build community best practices and represent the practice in internal and external technical forums.

Required Qualifications

Bachelor's degree in Computer Science, Computer Technical Architecting, Information Technology or a related Technical Architecting field.

Minimum 6 years of experience coding, building, monitoring, troubleshooting, designing and operating AI/ML infrastructure, cloud platforms, data platforms, model deployment pipelines or large-scale Technical Architecting solutions.

Strong understanding of AI/ML concepts and the compute, infrastructure, orchestration and deployment foundations required to run production AI systems.

Minimum 6 years of proficiency in programming or scripting languages such as Python, Java, C++, Bash, PowerShell or equivalent Technical Architecting languages.

Experience with data pipeline and workflow management tools such as Apache Airflow, Kubeflow, managed orchestration services or platform-native workflow tooling.

Proven experience leading AI infrastructure projects and teams, including technical direction, design reviews, delivery governance and stakeholder alignment.

Strong project management, communication, problem-solving and cross-functional collaboration skills in fast-paced client or enterprise environments.

Demonstrated experience evaluating and selecting AI technologies, frameworks, reference architectures and platform services for production solutions.

Required Skills/ Experience

Expert-level hands-on architecture experience with Databricks workspaces, clusters/serverless compute, jobs, MLflow, Model Registry, Unity Catalog, Delta Lake, Feature Technical Architecting and model-serving capabilities.

Deep knowledge of Spark-based distributed processing, training/model pipelines, lakehouse architecture, model deployment, data governance, observability and resilience Technical Architecting.

Strong experience with Python, SQL, Spark, Terraform/Databricks Asset Bundles, Git-based CI/CD, security guardrails, monitoring and platform cost optimization.

Ability to evaluate multiple Databricks architecture options and produce standards, patterns, decision records, benchmarks and executive-ready recommendations.

Experience applying MLOps/DataOps/InfraOps practices for experiment tracking, model registry, deployment automation, monitoring, incident response and rollback strategies.

Good to Have Skills

Databricks certifications such as Databricks Machine Learning Professional, Data Technical Architect Professional or related lakehouse architecture credentials.

Industry experience designing lakehouse and AI infrastructure for BFSI, healthcare, retail/e-commerce, telecom, manufacturing, energy or public sector environments with compliance, security and reliability constraints.

Exposure to LLMOps, vector search, retrieval pipelines, feature stores, GPU-backed model training, model optimization and low-latency model serving.

Experience with Unity Catalog governance, enterprise architecture roadmaps, vendor/partner management, FinOps and production support operating models.