Posted 13 August, 2026
Consultant
LatentView
Dublin, Dublin, Ireland
Full Time
Reference: 409823371
Designation : Consultant
Level : L3
Location : Dublin, Dublin , Ireland
Experience : 4 to 10 years
Job Role :
We are seeking an experienced AI & Data Engineering Resource Capability Specialist to lead workforce strategy, skill capability mapping, and resource management for our growing AI, Machine Learning, and Data Engineering practices.
In this role, you will sit at the intersection of technical delivery and strategic operations. You will be responsible for ensuring our projects have the right technical talent assigned at the right time, while actively designing learning paths to upskill teams on emerging technologies (LLMs, GenAI, MLOps, modern data stacks)
Responsibilities :
1. Technical Delivery & Project Execution
Delivery Oversight: Act as the technical anchor across active AI and Data Engineering engagements, ensuring architecture, data pipelines, and AI models meet quality standards and performance benchmarks.
Technical Risk Mitigation: Identify technical bottlenecks, data governance risks, and deployment blockers early in the project lifecycle, collaborating with engineering leads to implement fast solutions.
Standards & Best Practices: Enforce modern engineering frameworks across project teams, including CI/CD for ML (MLOps), code reviews, data testing, security protocols, and ethical AI standards.
2. Project Planning & Technical Scoping
Technical Roadmapping: Translate client business requirements into concrete AI and Data Engineering project plans, deliverables, technical milestones, and sprint backlogs.
Resource Architecture: Determine the exact technical skill mix, team topology, and tool stack needed to successfully execute specific project scopes.
Feasibility & Solutioning: Work with project leads during inception to validate technical feasibility, assess data maturity, and select appropriate platforms (e.g., cloud stack, vector databases, LLM orchestration frameworks).
3. Team Leadership, Mentorship & Technical Guidance
Hands-on Guidance: Provide day-to-day technical direction to Data Engineers, ML Engineers, and AI Developers, helping them solve complex architecture, ETL, and modeling challenges.
Code & Architecture Reviews: Lead technical reviews to ensure pipelines, API integrations, and AI models are scalable, efficient, and maintainable.
Team Empowerment: Establish a culture of technical excellence, psychological safety, and innovation, guiding the team through complex technical transitions (e.g., migrating to GenAI/RAG architectures).
4. Upskilling & Technical Capability Development
Technical Learning Frameworks: Design and lead structured technical upskilling programs to transition traditional Data Engineers into Modern Data & GenAI/ML Engineers.
Hands-on Labs & POCs: Drive internal technical initiatives, hackathons, and Proof-of-Concepts (POCs) to give engineers practical experience with emerging tools (e.g., LangChain, LlamaIndex, Databricks, Snowflake, MLOps tooling).
Certifications & Skill Progression: Establish clear technical competency paths and mentor team members through cloud platform, data engineering, and AI/ML industry certifications.
Required skills :
DE, AI strategy analyst (Python, SQL, AI Tools & Infra)
Level : L3
Location : Dublin, Dublin , Ireland
Experience : 4 to 10 years
Job Role :
We are seeking an experienced AI & Data Engineering Resource Capability Specialist to lead workforce strategy, skill capability mapping, and resource management for our growing AI, Machine Learning, and Data Engineering practices.
In this role, you will sit at the intersection of technical delivery and strategic operations. You will be responsible for ensuring our projects have the right technical talent assigned at the right time, while actively designing learning paths to upskill teams on emerging technologies (LLMs, GenAI, MLOps, modern data stacks)
Responsibilities :
1. Technical Delivery & Project Execution
Delivery Oversight: Act as the technical anchor across active AI and Data Engineering engagements, ensuring architecture, data pipelines, and AI models meet quality standards and performance benchmarks.
Technical Risk Mitigation: Identify technical bottlenecks, data governance risks, and deployment blockers early in the project lifecycle, collaborating with engineering leads to implement fast solutions.
Standards & Best Practices: Enforce modern engineering frameworks across project teams, including CI/CD for ML (MLOps), code reviews, data testing, security protocols, and ethical AI standards.
2. Project Planning & Technical Scoping
Technical Roadmapping: Translate client business requirements into concrete AI and Data Engineering project plans, deliverables, technical milestones, and sprint backlogs.
Resource Architecture: Determine the exact technical skill mix, team topology, and tool stack needed to successfully execute specific project scopes.
Feasibility & Solutioning: Work with project leads during inception to validate technical feasibility, assess data maturity, and select appropriate platforms (e.g., cloud stack, vector databases, LLM orchestration frameworks).
3. Team Leadership, Mentorship & Technical Guidance
Hands-on Guidance: Provide day-to-day technical direction to Data Engineers, ML Engineers, and AI Developers, helping them solve complex architecture, ETL, and modeling challenges.
Code & Architecture Reviews: Lead technical reviews to ensure pipelines, API integrations, and AI models are scalable, efficient, and maintainable.
Team Empowerment: Establish a culture of technical excellence, psychological safety, and innovation, guiding the team through complex technical transitions (e.g., migrating to GenAI/RAG architectures).
4. Upskilling & Technical Capability Development
Technical Learning Frameworks: Design and lead structured technical upskilling programs to transition traditional Data Engineers into Modern Data & GenAI/ML Engineers.
Hands-on Labs & POCs: Drive internal technical initiatives, hackathons, and Proof-of-Concepts (POCs) to give engineers practical experience with emerging tools (e.g., LangChain, LlamaIndex, Databricks, Snowflake, MLOps tooling).
Certifications & Skill Progression: Establish clear technical competency paths and mentor team members through cloud platform, data engineering, and AI/ML industry certifications.
Required skills :
DE, AI strategy analyst (Python, SQL, AI Tools & Infra)
