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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in leading AI project delivery, mentoring engineering teams, and establishing technical standards while ensuring production readiness and reliability of AI systems. Proficient in Python, MLOps, and cloud technologies, with a strong focus on evaluation design and communication with stakeholders.
Highest-signal resume keywords
AI Project DeliveryExpert Python ProficiencyMLOps/LLMOps ExperienceCloud Experience (AWS, Azure, GCP)Team Leadership and Mentoring
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
AI Solutions DeploymentAdvanced Prompt EngineeringTechnical Review and AssessmentEvaluation Framework DesignContainerisation and OrchestrationAPI DesignMicroservices DevelopmentStructured ExperimentationModel Failure IdentificationScalable Inference Infrastructure
Soft Skills
Clear CommunicationTeam BuildingMentoring
Tools & Technologies
MLflowWeights and BiasesGitCI/CD PipelinesEvent-Driven Architectures
Industry Keywords
AI EngineeringRAG SystemsLLM-Powered SolutionsTechnical GovernanceStakeholder Communication
Tech Stack
Tools & technologiesAWSAzureCloudGoogle Cloud PlatformMicroservicesPython
About the role
Key responsibilities & impact- Lead project delivery end to end, with governance, stakeholder communication, and accountability for outcomes
- Build and mentor a high-performing AI engineering team
- Establish technical standards and foster a culture of quality and pragmatism
- Own proposals and new business initiatives, defining technical feasibility and communicating risks and tradeoffs to clients
- Define realistic boundaries and expectations for AI systems
- Conduct technical reviews and architectural assessments
- Guide design and delivery of production-ready RAG systems, agentic frameworks, and LLM-powered solutions
- Lead advanced prompt engineering, including instruction design, few-shot sets, structured outputs, and tool/agent prompts
- Run feasibility assessments across prompting, RAG, fine-tuning, and classical ML
- Mentor engineers on AI system design and production deployment
- Design evaluation frameworks, including LLM-as-a-judge, recall@k, precision@k, and go/no-go gates
- Lead structured experiments across prompts, retrievers, chunking strategies, and models
- Establish practices for identifying and categorising model failures
- Set AI production reliability standards
- Build scalable inference infrastructure and CI/CD pipelines
- Automate the MLOps/LLMOps lifecycle, including tracking, versioning, deployment, monitoring, and retraining
- Design APIs, microservices, and orchestration layers for latency, cost, and reliability
- Lead infrastructure decisions balancing technical excellence and business efficiency
Requirements
What you’ll need- 5+ years building and deploying AI solutions in production environments
- Expert Python proficiency
- Strong Git practices
- Experience with ML/LLM versioning and deployment
- Solid cloud experience across AWS, Azure, or GCP; preference for Azure
- Containerisation and orchestration knowledge
- Hands-on RAG experience covering chunking, embeddings, retrieval, reranking, and evaluation
- Proven MLOps/LLMOps track record using tools like MLflow, Weights and Biases, or similar
- Practical evaluation design skills: metrics, dataset curation, and structured experimentation
- Experience with event-driven architectures, APIs, and microservices
- Clear communication with engineering teams and senior stakeholders
- Strong hiring and team-building instincts with proven mentoring experience
- English: Advanced (required for effective communication with global teams and client leadership)
- 2+ years of direct team leadership or technical management responsibility
Benefits
Comp & perks- Certifications in AWS, Databricks, and Snowflake
- Access to AI learning paths
- Study plans, courses, and additional certifications tailored to the role
- Access to Udemy Business
- English lessons
- Travel opportunities to attend industry conferences and meet clients
- Career development plans and mentorship programs
- Special day rewards for birthdays, work anniversaries, and other personal milestones
- Company-provided equipment
- Flexible working options
- Other benefits may vary according to location in LATAM