Machine learning Engineer II - GenAI studios

Job Summary: As a ML Engineer II, you will play a pivotal role in building end to end ML lifecycle with scale. Leveraging ML algorithms and A/B testing, we continuously refine our recommendations. If you're passionate about tackling complex technical challenges using big data, ML models, and building scalable services, you'll thrive here.   About the team: We’re an AI native, agile & fast-moving team focused on turning bold ideas into impactful products. From rapid prototyping of new concepts to building scalable platforms that power future growth, we thrive on experimentation and innovation. Our platform highlights creators and their narratives by giving them AI-powered systems using advanced machine learning and multimedia pipelines. As part of this team, you’ll work at the intersection of creativity and technology — building end-to-end experiences, experimenting with emerging tech, and shaping the future of entertainment content. Key Responsibilities:

Possess deep knowledge of core systems and takes full responsibility for the code, design, and project delivery, ensuring high quality and on-time completion

Build and evolve end-to-end ML lifecycle systems from data processing to model serving, using technologies spanning big data frameworks to ML platforms (TensorFlow, PyTorch)

Design and implement sophisticated monitoring systems that track both technical health and quality metrics

Create advanced experimentation frameworks that enable rapid A/B testing of algorithms

Design and develop low-latency, high-throughput serving systems with appropriate caching strategies

Establish MLOps best practices using MLflow for experiment tracking, model versioning, and deployment automation

Build stream processing pipelines for real-time feature computation that power dynamic content personalization

Scale and optimize training and serving of machine learning models, including specialized solutions for embedding scaling

Collaborate with Product Managers and cross-functional teams to understand requirements and deploy high-quality software solutions that meet business objectives

Skills & Attributes for Success

Strong coding skills in Python, Java, Golang, or Scala

Experience with Big Data frameworks like Spark and Kubernetes for data and ML workloads

Proficient in implementing API performance monitoring solutions using tools such as Grafana, Prometheus, and Cloudwatch

Skilled in developing end-to-end machine learning pipelines ensuring consistency between development and production environments

Ability to design scalable ML architectures tailored to site traffic and feature complexity for predictive algorithms

Familiar with model and data versioning, resource allocation and scaling, and logging to build optimal systems

Experienced in creating systems that monitor and react to faults in resources, data streams, and model responses

Expertise with MLOps tools for scalable, production-level deployment, including feature stores, model hosting and versioning, data versioning, prediction and drift monitoring, and automated remediation

Strong problem-solving skills and the ability to work effectively in a fast-paced environment

Commitment to continuous learning and professional development

Preferred Education & Experience

B.Tech/B.E/ Masters or equivalent degree. 

At least 2-4+ years of professional experience in building and operating machine learning systems at scale