Job Description

Job Description
You will be responsible for defining and leading product strategy for AI platforms, data infrastructure, and enterprise-scale data migration initiatives. The position combines a technical orientation with a product vision, collaborating with engineering teams and stakeholders to drive innovative, scalable solutions aligned with business objectives.
Responsibilities:
Define product strategy for AI platforms, data infrastructure, and enterprise-scale data migration initiatives.
Lead technical product discovery by evaluating emerging technologies such as GenAI, Agentic AI, vector databases and streaming architectures, assessing their suitability for customer use cases.
Design solution architectures in collaboration with architects and data engineers, making build or buy decisions and selecting appropriate technology stacks.
Develop technical roadmaps that balance innovation, scalability, security, and time to value.
Be product responsible for GenAI applications using language models, RAG architectures, Agentic frameworks and multi-modal artificial intelligence systems.
Translate business requirements into technical specifications, API contracts, data schemas, and systems integration patterns.
Guide model selection, evaluation criteria, and implementation strategies for machine learning models in production environments.
Drive MLOps practices, including model release, monitoring, performance tracking, and improvement.
Lead product planning for data lake and lakehouse implementations, data warehouse modernization, and migrations to cloud platforms.
Define data product requirements, including ingestion pipelines, transformation logic, quality rules, governance policies, and access patterns.
Oversee the integration of multiple data domains, ensuring interoperability, data lineage and metadata management.
Collaborate with data engineering teams on performance optimization, cost management, and scalability planning.
Maintain well-organized backlogs with correctly sized and technically detailed functionalities.
Work collaboratively with engineering teams to decompose complex functionalities into incremental deliverables with clear technical dependencies.
Define sprint goals aligned with quarterly goals and long-term product vision.
Conduct technical feasibility analysis, proof of concept, and experimental solutions to validate approaches before making full investments.
Analyze trade-offs between alternative technical solutions considering performance, cost, maintainability and developer experience.
Document technical decisions, architectural decision records, and design patterns to facilitate knowledge transfer.
Communicate technical strategies and recommendations to executive stakeholders clearly and convincingly.
Requirements:
Bachelor's degree in technology or a business-related field, a master's degree is highly valued.
Between five and seven or more years of experience in technical product management, solutions architecture or software engineering.
More than five years of experience in product management positions.
Between three and five or more years of experience in artificial intelligence products, machine learning, generative artificial intelligence or data platforms.
Between three and five or more years of experience working in Agile or Scrum environments, with mastery of Agile methodologies and ceremonies.
Knowledge of cloud architectures such as AWS, Azure, and GCP, as well as modern data platform technologies.
Experience in artificial intelligence and GenAI, including language model integration, prompt engineering, RAG architectures, fine-tuning and Agentic artificial intelligence frameworks such as LangChain, LlamaIndex or AutoGen.
Experience in data engineering, including ETL and ELT patterns, data modeling, Snowflake, Databricks, dbt, Airflow and streaming architectures with Kafka.
Experience with cloud platforms such as AWS, including SageMaker, Bedrock and Glue; Azure, including OpenAI Service and Synapse; and GCP, including Vertex AI and BigQuery.
Knowledge of MLOps, including model deployment, monitoring, version control, integration and delivery for machine learning, feature stores and experiment tracking.
Experience in data migration, including evaluation methodologies, migration patterns, data validation, and transition strategies.
Knowledge of good development practices such as API design, microservices, containerization with Docker and Kubernetes, and continuous integration and delivery pipelines.
Demonstrated ability to design solutions and technical architecture.
Ability to translate business needs into clear technical specifications.
Analytical and problem-solving skills in complex technical environments.
Experience managing both technical and non-technical stakeholders.
Clear technical communication skills, including documentation of complex systems and presentation of architectural decisions.
Ability to identify risks, map dependencies and plan mitigation strategies.
Experience in software development or data engineering of more than three years.
Experience in consulting or professional services, delivering solutions to clients.
Certifications such as AWS Solutions Architect, Azure Data Engineer, GCP Professional Data Engineer or Scrum Certified Product Owner are valued.
Constant curiosity about emerging technologies and an experimentation mentality.
Capacity for team collaboration and development in multifunctional environments.

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