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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and deploying AI solutions, with a strong focus on MLOps/LLMOps practices, technical leadership, and effective communication with stakeholders. Proven ability to mentor teams and establish high standards for AI system design and production reliability.
Highest-signal resume keywords
AI Solution DeploymentExpert 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 System DesignPrompt EngineeringTechnical ReviewEvaluation Framework DesignContainerisationOrchestrationAPI DesignMicroservicesCI/CD Pipeline DevelopmentEvent-Driven Architecture
Soft Skills
Clear CommunicationTeam BuildingMentoring
Tools & Technologies
MLflowWeights and BiasesGitAzureAWSGCP
Industry Keywords
RAG SystemsLLM-Powered SolutionsStructured ExperimentationModel Evaluation MetricsProduction Reliability Standards
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 a culture of quality and pragmatism
- Own proposals and new business initiatives
- Define technical feasibility and communicate risks and tradeoffs to clients
- Define appropriate AI system scope and set realistic expectations
- 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, with 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, including 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
- Proven mentoring experience
- 2+ years of direct team leadership or technical management responsibility
- Advanced English required for communication with global teams and client leadership
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 to support professional communication
- 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