Senior, Principal Technical Solution Owner AI

About Provectus

At Provectus, we architect enterprise-grade AI and data solutions that transform how organizations leverage their most valuable asset – data. We combine deep technical expertise with strategic product thinking to deliver scalable, production-ready AI systems and modern data platforms.

Who We're Looking For

We're seeking a Senior Technical Product Manager with strong engineering acumen and product leadership experience to drive sophisticated AI and data platform initiatives. You bring the technical depth to engage in architecture discussions, evaluate trade-offs, and make informed decisions about complex system designs – while maintaining focus on business value and user outcomes.

You're a technical translator who bridges the gap between possibility and practicality, helping clients navigate the rapidly evolving AI landscape with confidence. Your background allows you to assess technical feasibility, identify risks early, and guide engineering teams toward optimal solutions.

• Architect Product Strategy for Technical Platforms:
• Define product strategy for AI platforms, data infrastructure, and enterprise-scale data migration initiatives.
• Lead technical product discovery – evaluating emerging technologies (GenAI, Agentic AI, vector databases, streaming architectures) and assessing fit for client use cases.
• Design solution architectures in collaboration with data architects and engineers, making build-vs-buy decisions and technology stack selections.
• Develop technical roadmaps balancing innovation, scalability, security, and time-to-value.

• Own 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/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 properly sized, 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 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 (ADRs), and design patterns for knowledge sharing.
• Communicate technical strategies and recommendations to executive stakeholders with clarity and conviction.

• Required Qualifications:
• Bachelor's degree in Technology or Business related field (Master's preferred).
• 5-7+ years of experience in technical product management, solutions architecture, or software engineering.
• 5+ years in product management roles with demonstrated end-to-end product ownership.
• 3-5+ years of experience with AI/ML products, Generative AI, or data platform development.
• 3-5+ years working in Agile/Scrum environments with strong command of Agile methodologies and ceremonies.
• Deep understanding of cloud architectures (AWS, Azure, GCP) and modern data stack technologies.

• AI/GenAI: LLM integration, prompt engineering, RAG architectures, fine-tuning, Agentic AI frameworks (LangChain, LlamaIndex, AutoGen).
• Data Engineering: ETL/ELT patterns, data modeling, Snowflake, Databricks, dbt, Airflow, Kafka/streaming architectures.
• Cloud Platforms: AWS (SageMaker, Bedrock, Glue), Azure (OpenAI Service, Synapse), GCP (Vertex AI, BigQuery).
• MLOps: Model deployment, monitoring, versioning, CI/CD for ML, feature stores, experiment tracking.
• Data Migration: Assessment methodologies, migration patterns, data validation, cutover strategies.
• Development Practices: API design, microservices, containerization (Docker, Kubernetes), CI/CD pipelines.

• Solution design and technical architecture capabilities.
• Requirements translation from business needs to technical specifications.
• Strong analytical thinking and problem-solving in complex technical domains.
• Exceptional stakeholder management across technical and non-technical audiences.
• Clear technical communication—documenting complex systems and presenting architectural decisions.
• Risk identification, dependency mapping, and mitigation planning.

• Prior software development or data engineering experience (3+ years).
• Background in consulting or professional services, delivering client solutions.
• Certifications: AWS Solutions Architect, Azure Data Engineer, GCP Professional Data Engineer, Certified Scrum Product Owner.

• Insatiable curiosity about emerging technologies and a hands-on experimentation mindset.
• Close attention to detail with quality focus and commitment to technical excellence.
• Collaborative team player who thrives in cross-functional environments.
• Adaptable and comfortable navigating ambiguity in fast-paced consulting contexts.
• Passion for mentoring engineers and elevating technical practices.

• Lead top-tier engineering teams and cutting-edge agentic AI systems, enterprise AI platforms.
• Shape how enterprises adopt AI — from strategy to architecture to delivery.
• Grow within a team building modern AI-delivery practices, tools, and frameworks.
• Remote-friendly culture with strong engineering, data, and consulting partnerships.