Applied AI Engineer II at Jobgether

, , India -

Full Time

Start Date

Immediate

Expiry Date

02 Oct, 26

Salary

0.0

Posted On

04 Jul, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

MLOps, Machine Learning Engineering, Python, Kubernetes, Docker, CI/CD, CUDA, AWS, GCP, Azure, Linux, Data Pipelines, Model Monitoring, Model Deployment, Full-stack ML Development, Scalable Infrastructure

Industry

Internet Marketplace Platforms

Description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an Applied AI Engineer II based in India. This is a hands-on, production-focused ML engineering role where you will help power the infrastructure behind large-scale AI systems used by millions of users worldwide. You will sit at the intersection of machine learning research and production engineering, ensuring models move seamlessly from experimentation to reliable, scalable deployment. The role involves working closely with data scientists, ML researchers, and software engineers to build and maintain robust MLOps pipelines and infrastructure. You will contribute to the stability, performance, and scalability of ML systems running in production environments. A key part of your work will involve troubleshooting across the full stack—from infrastructure layers like Kubernetes and Docker to application-level ML services. You will also help shape CI/CD practices and improve monitoring, deployment, and evaluation workflows. This is an ideal opportunity for an engineer passionate about applied machine learning systems and building infrastructure that directly impacts real-world AI products. \n Accountabilities: Collaborate with data scientists and engineers to build and improve scalable data pipelines, model training workflows, and production-grade ML systems. Own troubleshooting and optimization across the full ML stack, including infrastructure layers (Linux, Docker, Kubernetes) and application-level services. Support the design and development of on-premises MLOps solutions to ensure smooth transition from research to production deployment. Build and maintain tools for model deployment, monitoring, observability, and operational reliability of ML systems. Improve CI/CD pipelines to support continuous integration, testing, and deployment of machine learning models and services. Optimize ML models for performance, throughput, and scalability in production environments. Maintain high engineering standards through code reviews, testing practices, and consistent integration across ML systems. Requirements: 3+ years of experience in MLOps, machine learning engineering, or full-stack ML development roles. Strong programming skills in Python and familiarity with the scientific Python stack; exposure to CUDA is a plus. Solid understanding of the ML lifecycle, including training, validation, deployment, and monitoring in production environments. Hands-on experience with Kubernetes, cloud platforms (AWS, GCP, or Azure), and CI/CD systems. Experience working with ML frameworks and data pipelines in production settings. Strong problem-solving mindset with a willingness to dive deep into infrastructure and system-level issues. Excellent communication and collaboration skills, with a strong focus on team-based engineering. Passion for machine learning systems and scaling AI solutions effectively in real-world environments. Benefits: Competitive salary with annual performance-based bonus. Comprehensive medical, life, and accidental insurance coverage. Generous leave policies including vacation, parental leave, menstrual leave, and flexible time off. Remote-first work environment with flexible working hours. Learning and development support through training budgets, workshops, and education reimbursements. Tech and work-from-home stipends along with new hire setup allowances. Employee referral program and additional performance-based incentives. Access to premium platform tools and company-wide events (virtual and onsite). \n How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether? Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time. #LI-CL1

Responsibilities

Build and maintain scalable MLOps pipelines and infrastructure to move AI models from experimentation to production. Troubleshoot and optimize the full ML stack, including Kubernetes, Docker, and application-level services.