Technical Product Manager

We are seeking a dynamic and deeply technical leader for a unique dual role that combines strategic product management with hands-on technical expertise. As a Senior Technical Product Manager, you will not only define the vision and roadmap for our cutting-edge Generative AI and Machine Learning Platform but also serve as a subject matter expert and hands-on contributor in a core engineering discipline.

You will collaborate with Product Owners, Tech Anchors, and world-class engineers to build the foundational tools, pipelines, and infrastructure that empower data scientists and developers across the enterprise. This role is for the product leader who loves to stay close to the code, architect solutions, and solve complex engineering challenges in the AI/ML space.

What You'll Do (Core Technical Product Management Responsibilities):

Product Vision & Strategy: Partner with the Product Owner to translate the high-level vision, road mapping for our GenAI products or platform into a clear, actionable strategy and a prioritized backlog of user stories Define and manage the product roadmap, focusing on delivering tangible value through iterative development and deliberate prioritization. Leverage latest of Google Cloud services and Kubernetes technologies. Grow technical capabilities / expertise and provide guidance to other members on the team. 

Stakeholder Collaboration & Technical Influencer: Act as the primary technical liaison between the development team and stakeholders, translating business needs into technical requirements and vice-versa. Champion and help standardize best practices for MLOps and Generative AI development, including RAG (Retrieval-Augmented Generation), fine-tuning, and agentic workflows. Experiment with emerging technologies and share your knowledge to elevate the entire team. Lead by example in use of Paired Programming for cross training/upskilling, problem solving, and speed to delivery.

Agile Leadership & Delivery: Facilitate Agile ceremonies (sprint planning, stand-ups, retrospectives) and lead technical working sessions to unblock the team and drive progress. Work with software and ML engineers to tackle challenging MLOps problems. Work with the team to help build tools/ML Pipeline and systems using Python that make data scientists happier and more productive. Focus on delivering product value through careful and deliberate prioritization. Facilitate Agile ceremonies with the product team and working sessions with the stakeholder group(s). Help innovate standardize machine learning development practices. Experiment, innovate and share knowledge with the team. 

Specialization (Your Area of Deep Expertise):

In addition to your product duties, you will be a hands-on expert and key contributor in one of the following domains. You will dedicate a portion of your time to architecture, coding, and solving critical problems within your chosen specialty.

1. AI Ops / MLOps Expert:

• Design and implement CI/CD/CT pipelines for automated model training, validation, and deployment.

• Architect and manage scalable infrastructure on GCP and Kubernetes (GKE) for model serving and GPU workloads.

• Implement robust monitoring, logging, and alerting for model performance, data drift, and system health.

2. Software Engineering (Scale) Expert:

• Develop, optimize, and productionize complex ML and LLM-based applications.

• Implement advanced RAG pipelines, including chunking strategies, vectorization, and integration with vector databases (e.g., Elastic, PgVector).

• Architect and build high-throughput, low-latency microservices and REST APIs to serve models and data.

• Own the design of resilient, scalable backend systems capable of handling enterprise-level traffic.

• Champion elite coding practices, including Test-Driven Development (TDD), paired programming, and rigorous code reviews.

3. Data Engineering Expert:

• Design and build scalable data ingestion and processing pipelines using tools like Spark, Kafka, and Beam.

• Engineer data models and warehousing solutions optimized for large-scale analytics and ML training datasets.

• Ensure data quality, governance, and security across all data-centric platform components.

4. Solution Architecture Expert:

• Design end-to-end technical blueprints for complex AI/ML solutions on Google Cloud Platform (GCP).

• Evaluate and select the optimal combination of cloud services (e.g., Vertex AI, BigQuery, Cloud Functions, GKE) to meet product requirements.

• Ensure solutions are secure, scalable, cost-effective, and aligned with enterprise architecture standards.

5. Python Full Stack Expert:

• Develop both backend services (using FastAPI, Flask) and frontend interfaces (using React, NextJs or similar frameworks) for our AI platform and tools.

• Build intuitive UIs and interactive applications that make data scientists more productive and happier.

• Own the end-to-end development of internal applications from database to browser.