You will be responsible for building and scaling the foundational AI platform that enables production-ready AI solutions across the organization. Working at the intersection of AI infrastructure, software engineering, and systems integration, you will help establish the tools, frameworks, and integrations required to deploy and operate AI and GenAI applications at scale.
What You'll Do:
- Build & Scale the AI Platform Foundation
- Design, build, and maintain the ML platform from the ground up, including experiment tracking, model registries, model serving, CI/CD pipelines, and monitoring capabilities.
- Establish scalable infrastructure standards and engineering best practices that enable rapid AI solution development and deployment.
- Contribute to the creation of reusable AI platform components that support multiple use cases across the organization.
- Drive platform reliability, observability, security, and scalability from design through production. Develop AI & GenAI Infrastructure
- Own and enhance the infrastructure supporting GenAI applications, including vector databases, LLM serving frameworks, retrieval systems, and evaluation frameworks.
- Design and implement scalable Retrieval-Augmented Generation (RAG) architectures and supporting infrastructure.
- Build and maintain evaluation, monitoring, and observability frameworks for AI and GenAI systems.
- Support the deployment and operationalization of both classical machine learning models and GenAI applications. Build Data Pipelines & Production Integrations
- Design and implement robust data pipelines that power machine learning and GenAI solutions in production environments.
- Develop integration layers that reliably connect AI services and outputs with existing enterprise systems and business applications.
- Build APIs, services, and distributed system components that support scalable AI product delivery.
- Collaborate closely with AI engineers, software engineers, product teams, and business stakeholders to ensure seamless adoption of AI solutions. Enable AI Use Case Delivery
- Partner with cross-functional teams to accelerate the deployment of AI use cases into production.
- Ensure AI solutions meet requirements for reliability, performance, security, and maintainability.
- Support the evaluation and adoption of emerging AI technologies, tools, and frameworks.
- Contribute to the continuous improvement of the AI development ecosystem.
What It Takes:
- 3+ years of experience in ML Platform Engineering, AI Infrastructure Engineering, MLOps, Software Engineering, or related fields.
- Hands-on experience building and maintaining ML platforms, including experiment tracking, model registries, model serving, and ML CI/CD pipelines.
- Strong experience with ML infrastructure tools such as MLflow, Weights & Biases (W&B), or similar platforms.
- Experience building and supporting GenAI infrastructure, including vector databases (Pinecone, Weaviate, pgvector), LLM serving frameworks, and RAG architectures.
- Knowledge of AI evaluation and observability tools such as RAGAS, LangSmith, or equivalent solutions.
- Experience working with cloud-native AI and ML services, preferably AWS SageMaker.
- Strong software engineering skills, including API development, distributed systems design, and backend application development.
- Hands-on experience with containerization and orchestration technologies such as Docker and Kubernetes.
- Experience integrating AI systems with enterprise applications through REST APIs, message queues, and service-based architectures.
- Understanding of infrastructure automation, monitoring, scalability, and production-grade system design. Collaboration & Problem Solving
- Ability to work effectively within Agile teams and cross-functional environments.
- Strong analytical and systematic problem-solving capabilities.
- Ability to balance technical excellence with practical business outcomes.
- Effective communication skills and the ability to collaborate across global teams and functions.
- Comfortable operating in fast-paced environments where processes and platforms are still evolving. Mindset
- Builder mentality with a passion for creating scalable platforms and systems from the ground up.
- Strong ownership mindset with a focus on delivering reliable and maintainable solutions.
- Curiosity to explore emerging AI technologies while maintaining engineering rigor.
- Systems-thinking approach that prioritizes scalability, reliability, observability, and long-term maintainability.
- Passion for enabling others by creating platforms and tools that accelerate innovation.
- You have built or significantly contributed to an ML platform in a production environment.
- You have successfully productionized both traditional machine learning models and GenAI applications end-to-end.
- You naturally think about scalability, reliability, monitoring, and observability before implementation begins.
- You are equally comfortable building AI infrastructure and developing the software integrations that connect AI capabilities to real business systems.
- You enjoy solving complex engineering challenges while creating foundations that other teams can build upon.
Location: Hyderabad, India (Onsite)
Skills Required
- 3+ years of experience in ML Platform Engineering, AI Infrastructure Engineering, MLOps, or related fields
- Hands-on experience building and maintaining ML platforms (experiment tracking, model registries, model serving, ML CI/CD pipelines)
- Experience with ML infrastructure tools such as MLflow or Weights & Biases (W&B)
- Experience building and supporting GenAI infrastructure, including vector databases (Pinecone, Weaviate, pgvector) and LLM serving / RAG architectures
- Knowledge of AI evaluation and observability tools such as RAGAS or LangSmith
- Experience with cloud-native AI/ML services (preferably AWS SageMaker)
- Strong software engineering skills including API development, distributed systems design, and backend application development
- Hands-on experience with containerization and orchestration (Docker, Kubernetes)
- Experience integrating AI systems with enterprise applications using REST APIs, message queues, and service-based architectures
- Understanding of infrastructure automation, monitoring, scalability, and production-grade system design
Protolabs Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Protolabs and has not been reviewed or approved by Protolabs.
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Healthcare Strength — Medical, dental, and vision options are broadly available and described as good to excellent. Additional protections like short- and long-term disability and life insurance bolster the overall package.
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Leave & Time Off Breadth — A starting PTO allotment plus paid holidays, along with added wellness and volunteer time, is emphasized, with some roles noting PTO growth over tenure. Paid caregiver leave appears in postings and supports flexibility for life events.
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Retirement Support — A 401(k) with company match and immediate vesting is offered, supporting long-term savings. This foundation is frequently cited alongside core financial benefits as a strong element of total rewards.
Protolabs Insights
What We Do
Protolabs is the world's fastest digital manufacturing source for rapid prototyping and on-demand production. The technology-enabled company produces custom parts and assemblies in as fast as 1 day with automated 3D printing, CNC machining, sheet metal fabrication, and injection molding processes. Our digital approach to manufacturing enables accelerated time to market, reduces development and production costs, and minimizes risk throughout the product life cycle. 3D Printing Our 3D printing service offers a wide selection of materials and technologies to create prototypes and end-use parts with complex geometries and detailed features. With tight process controls, careful design reviews, and extensive quality monitoring, we ensure precise and repeatable 3D-printed parts, every time. CNC Machining We use 3- and 5-axis milling along with turning to machine parts from commercial-grade plastics and metals. Our online quoting system and automated manufacturing process enable us to ship parts within 24 hours, helping customers accelerate development and reduce time to market. Sheet Metal Fabrication Protolabs is an industry leader in quick-turn sheet metal parts for both prototyping and low-volume production. Our digital approach to manufacturing enables us to fabricate sheet metal parts in as fast as 5 days. Additionally, we can support our customers’ development efforts with component assemblies, several finish options, and screen printing. Injection Molding Our injection molding service offers two options—prototyping and on-demand manufacturing—which provide customers a tooling solution that aligns with their project’s requirements. It’s used for quick-turn prototyping, bridge tooling, and low-volume production of up to 10,000+ parts in 15 days or less.
