Remote Senior Technical Solution Owner AI & Data Platforms

• 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 for client use cases.
• Design solution architectures in collaboration with data architects and engineers, including build-vs-buy decisions and technology stack selections.
• Develop technical roadmaps that balance innovation, scalability, security, and time-to-value.
• Own the end-to-end product lifecycle for GenAI applications leveraging LLMs, RAG architectures, Agentic frameworks, and multi-modal AI systems.
• Translate business requirements into technical specifications, API contracts, data schemas, and system integration patterns.
• Guide model selection, evaluation criteria, and deployment strategies for ML models in production environments.
• Champion MLOps practices, including model versioning, monitoring, performance tracking, and continuous improvement loops.
• Lead product planning for data lake and lakehouse implementations, warehouse modernizations, and cloud data platform migrations.
• Define data product requirements, including ingestion pipelines, transformation logic, data quality rules, governance policies, and access patterns.
• Oversee integration of multiple data domains, ensuring interoperability, data lineage, and metadata management.
• Partner with data engineering teams on performance optimization, cost management, and scalability planning.
• Facilitate Agile ceremonies and maintain well-groomed backlogs with technically detailed features and epic-level stories.
• Work closely with engineering teams to decompose complex features into incremental releases with clear technical dependencies.
• Define sprint goals aligned with quarterly objectives and the long-term product vision.
• Balance technical debt management with feature delivery, advocating for enablers and architectural improvements.
• Conduct technical due diligence, proofs-of-concept, and spike solutions to validate approaches before full investment.
• Analyze trade-offs between competing technical solutions, considering performance, cost, maintainability, and developer experience.
• Document technical decisions, architectural decision records, and design patterns for knowledge sharing.
• Communicate technical strategies and recommendations to executive stakeholders with clarity and conviction.

Technologies:

• AI
• Airflow
• API
• AWS
• Architect
• Azure
• CI/CD
• Cloud
• Databricks
• Docker
• ETL
• GCP
• Kafka
• Kubernetes
• LLM
• Product Manager
• Product Owner
• Security
• Snowflake
• dbt
• microservices
• BigQuery
• Support

More:

We are Provectus, where we architect enterprise-grade AI and data solutions that help organizations unlock the value of their data through scalable, production-ready AI systems and modern data platforms. We are seeking a Senior Technical Product Manager who can combine engineering depth with product leadership to drive complex AI and data platform initiatives. This is a remote-friendly role within a collaborative environment that partners closely with engineering, data, and consulting teams, offering the opportunity to shape how enterprises adopt AI from strategy through delivery while growing with a team building modern AI delivery practices, tools, and frameworks.

• Bachelors degree in Technology or a Business-related field; a Masters degree is preferred.
• 5-7+ years of experience in technical product management, solutions architecture, or software engineering.
• 5+ years of experience in product management roles with end-to-end product ownership.
• 3-5+ years of experience with AI/ML products, Generative AI, or data platform development.
• 3-5+ years of experience working in Agile/Scrum environments with strong command of Agile methodologies and ceremonies.
• Deep understanding of cloud architectures, including AWS, Azure, and GCP, and modern data stack technologies.
• Experience in AI/GenAI, including LLM integration, prompt engineering, RAG architectures, fine-tuning, and Agentic AI frameworks such as LangChain, LlamaIndex, and AutoGen.
• Experience in data engineering, including ETL/ELT patterns, data modeling, Snowflake, Databricks, dbt, Airflow, and Kafka/streaming architectures.
• Experience with cloud platforms such as AWS, Azure, and GCP.
• Experience with MLOps, including model deployment, monitoring, versioning, CI/CD for ML, feature stores, and experiment tracking.
• Experience with data migration methodologies, migration patterns, data validation, and cutover strategies.
• Experience with development practices such as API design, microservices, containerization with Docker and Kubernetes, and CI/CD pipelines.
• Strong solution design and technical architecture capabilities.
• Ability to translate business needs into technical specifications.
• Strong analytical thinking and problem-solving skills in complex technical domains.
• Exceptional stakeholder management across technical and non-technical audiences.
• Clear technical communication skills, including documenting complex systems and presenting architectural decisions.
• Ability to identify risks, map dependencies, and plan mitigations.
• Prior software development or data engineering experience is preferred.
• Background in consulting or professional services delivering client solutions is preferred.
• Relevant certifications such as AWS Solutions Architect, Azure Data Engineer, GCP Professional Data Engineer, or Certified Scrum Product Owner are preferred.
• Strong curiosity about emerging technologies and a hands-on experimentation mindset.
• Strong attention to detail, quality focus, and commitment to technical excellence.
• Collaborative team-player mindset in cross-functional environments.
• Comfort navigating ambiguity in fast-paced consulting contexts.
• Passion for mentoring engineers and elevating technical practices.