AI Data Engineer

Role Overview
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)

Work arrangement
Yes