AI Practice Engineer – DXG Center of Excellence (COE)

🧩 About the Role

A – Job Description:

The AI Practice Engineer is a senior-level Solution Architect with over 8 years of experience in software engineering, including at least 4 years in AI/Data Science. The role leads the design and implementation of AI solutions using cloud-based AI services and Microsoft Copilot, aligning tech strategies with business goals and best practices.

B – Key Responsibilities:

• Align technology with business goals to deliver outcomes for both the company and its clients.

• Promote best practices across the full lifecycle – from design to delivery – to minimize technical debt.

• Drive innovation by adopting emerging technologies.

• Lead by example through direct involvement in project delivery and mentorship.

C – Core Duties:

• Practice Development: Design and implement end-to-end AI practices including solutions, tools, and processes.

• Pre-Sales Support: Design solutions, prepare work breakdowns, and support estimations/bids while promoting AI capabilities to clients.

• Project Delivery: Play a key role in execution and support the team in maintaining alignment with tech best practices.

D – Preferred Background:

🧠 Knowledge:

• AI/ML Lifecycle: Data prep, model training, deployment, monitoring (MLOps), and AI-driven automation (IPA, predictive analytics, autonomous systems)

• Software Engineering: Scalable software application development

• Data Engineering: Building pipelines using Kafka, Flink, Spark; data orchestration with Airflow/Prefect; SQL/NoSQL database optimization

• Architecture & Design: Microservices, event-driven systems, serverless architectures, automation frameworks (IaC, self-healing systems)

• Security & Compliance: Knowledge of data governance, GDPR, SOC 2, compliance monitoring/reporting

• Emerging Tech: Hyperautomation, low-code/no-code integration with AI

🧪 Experience:

• 8+ years in tech roles, 4+ years in AI/ML or Data Science

• Proven delivery of AI solutions (chatbots, fraud detection, workflow optimization)

• Hands-on with scalable data platforms & predictive pipelines

• Systems integration & deployment of AI automation

• Mentoring teams and embedding best practices in AI projects

🧠 Competencies:

• System Thinking: Designing AI solutions that integrate into larger ecosystems

• Analytical Skills: Solving complex automation/data problems with scalability in mind

• Agility: Staying ahead in dynamic cloud/AI/automation tech landscapes

👤 Personal Traits:

• Detail-oriented, security-focused

• Lifelong learner, applies tech to real-world challenges

• Collaborative, can build strong internal/external relationships

• Ethics-driven, committed to responsible AI practices

• Resilient, delivers under pressure

• Forward-thinking, envisions how AI transforms business

Requirements

🧩 About the Role

A – Job Description:

The AI Practice Engineer is a senior-level Solution Architect with over 8 years of experience in software engineering, including at least 4 years in AI/Data Science. The role leads the design and implementation of AI solutions using cloud-based AI services and Microsoft Copilot, aligning tech strategies with business goals and best practices.

B – Key Responsibilities:

• Align technology with business goals to deliver outcomes for both the company and its clients.

• Promote best practices across the full lifecycle – from design to delivery – to minimize technical debt.

• Drive innovation by adopting emerging technologies.

• Lead by example through direct involvement in project delivery and mentorship.

C – Core Duties:

• Practice Development: Design and implement end-to-end AI practices including solutions, tools, and processes.

• Pre-Sales Support: Design solutions, prepare work breakdowns, and support estimations/bids while promoting AI capabilities to clients.

• Project Delivery: Play a key role in execution and support the team in maintaining alignment with tech best practices.

D – Preferred Background:

🧠 Knowledge:

• AI/ML Lifecycle: Data prep, model training, deployment, monitoring (MLOps), and AI-driven automation (IPA, predictive analytics, autonomous systems)

• Software Engineering: Scalable software application development

• Data Engineering: Building pipelines using Kafka, Flink, Spark; data orchestration with Airflow/Prefect; SQL/NoSQL database optimization

• Architecture & Design: Microservices, event-driven systems, serverless architectures, automation frameworks (IaC, self-healing systems)

• Security & Compliance: Knowledge of data governance, GDPR, SOC 2, compliance monitoring/reporting

• Emerging Tech: Hyperautomation, low-code/no-code integration with AI

🧪 Experience:

• 8+ years in tech roles, 4+ years in AI/ML or Data Science

• Proven delivery of AI solutions (chatbots, fraud detection, workflow optimization)

• Hands-on with scalable data platforms & predictive pipelines

• Systems integration & deployment of AI automation

• Mentoring teams and embedding best practices in AI projects

🧠 Competencies:

• System Thinking: Designing AI solutions that integrate into larger ecosystems

• Analytical Skills: Solving complex automation/data problems with scalability in mind

• Agility: Staying ahead in dynamic cloud/AI/automation tech landscapes

👤 Personal Traits:

• Detail-oriented, security-focused

• Lifelong learner, applies tech to real-world challenges

• Collaborative, can build strong internal/external relationships

• Ethics-driven, committed to responsible AI practices

• Resilient, delivers under pressure

• Forward-thinking, envisions how AI transforms business