What do we do ?

MediaMint is an AI-powered Revenue Operations services company dedicated to accelerating innovation and revenue growth for platforms and publishers worldwide. Our mission is to enhance operational efficiency, scalability, customer satisfaction, and data-driven insights through advanced technology, automation, and AI capabilities. We serve industry leaders across AdTech, Consumer, CTV, Publishing, and Retail.

Job Description :

We're looking for an engineer-researcher hybrid who is equally comfortable whiteboarding novel model architectures and writing production-grade code. You will prototype cutting-edge AI solutions, harden them for scale, and own the platform primitives-data, services, tooling-that unlock rapid experimentation and reliable delivery.

What You'll Do :

- Research & Prototyping : Rapidly explore and benchmark new ML/GenAI techniques; build proof-of-concepts that demonstrate business value.

- Platform Architecture : Design microservices, data pipelines, and model-serving infrastructure that meet demanding performance, latency, and cost targets.

- MLOps & Developer Experience : Build CI/CD, automated testing, and observability for models and services using GitHub Actions/Jenkins, Terraform, Kubernetes, and Prometheus/Grafana.

- Performance Engineering : Profile and optimise model inference (GPU/TPU), data throughput, and overall system reliability; run chaos experiments and capacity planning.

- Security & Compliance : Embed DevSecOps, data-privacy, and governance controls throughout the ML lifecycle.

- Cross-Functional Collaboration : Partner with data scientists, product managers, and revenue-ops SMEs to translate research into scalable features and insights.

- Thought Leadership : Contribute to technical roadmaps, publish internal papers, mentor engineers, and represent MediaMint in open-source and community forums.

What You Must Bring :

- 10+ years of software engineering with significant experience in AI/ML systems (training, inference, data engineering, or platform).

- Proven coding expertise in Python (strongly preferred) and at least one of Go, Rust, or Java; solid Linux fundamentals.

- Full-stack development skills spanning modern frontend frameworks (React/TypeScript), scalable REST/GraphQL APIs, and cloud-native backend services; ability to ship end-to-end features rapidly.

- Deep understanding of at least one cloud provider (AWS, GCP, or Azure) and containerised deployment with Docker & Kubernetes.

- Hands-on experience with ML frameworks (PyTorch, TensorFlow, JAX) and model-serving stacks (Triton, TorchServe, KServe).

- Infrastructure-as-Code proficiency (Terraform/Pulumi/CloudFormation) and CI/CD ownership.

- Strong analytical and problem-solving skills across network, compute, data, and application layers; ability to debug complex distributed systems.

- Excellent written and verbal communication; ability to explain research trade-offs to technical and non-technical stakeholders.

Bonus Points :

- Publications or patents in ML/GenAI.

- Experience with vector databases, feature stores, or online/offline experimentation platforms.

- Familiarity with AdTech, MarTech, or RevenueOps domains.