MLOps / ML Platform Engineer (LLM & Streaming Infra)
Skills
About This Role
Overview
We are seeking a highly skilled Machine Learning Platform Engineer to design, build, and scale the infrastructure powering modern AI and real-time data applications.
This role sits at the intersection of MLOps, platform engineering, DevOps, and Generative AI infrastructure, enabling data scientists and AI engineers to deploy production-grade machine learning and LLM-powered systems efficiently and securely.
The ideal candidate has strong experience building scalable ML platforms, supporting streaming architectures, and deploying Large Language Model (LLM) applications in production environments.
You will play a critical role in creating reliable AI infrastructure that supports high-performance inference, real-time data pipelines, and conversational AI systems.
ML & GenAI Platform Engineering
- Design, develop, and maintain scalable infrastructure for machine learning and Generative AI workloads.
- Build and optimize LLM infrastructure for training, fine-tuning, inference, and deployment.
- Support the deployment and orchestration of AI models across cloud and on-premise environments.
- Implement GPU-enabled infrastructure and workload optimization for high-performance AI applications.
- Develop reusable tooling, frameworks, and automation to accelerate ML experimentation and productionization.
Chat & Conversational AI Systems
- Design and support chat-based AI architectures, including conversational workflows, orchestration layers, memory management, and retrieval pipelines.
- Build infrastructure supporting AI assistants, copilots, and real-time conversational applications.
- Integrate vector databases, embeddings pipelines, and Retrieval-Augmented Generation (RAG) systems.
- Support prompt management, model routing, observability, and evaluation frameworks for LLM applications.
Streaming & DevOps Engineering
- Build and maintain DevOps pipelines for real-time streaming applications and event-driven systems.
- Manage CI/CD workflows for machine learning and distributed streaming services.
- Design resilient infrastructure for low-latency, high-throughput data processing workloads.
- Implement infrastructure-as-code, monitoring, logging, and automated deployment strategies.
- Ensure platform reliability, scalability, security, and operational excellence across environments.
Collaboration & Platform Enablement
- Partner with ML Engineers, Data Scientists, Software Engineers, and Product teams to deliver scalable AI solutions.
- Establish best practices for MLOps, platform governance, security, and infrastructure reliability.
- Drive platform standardization, automation, and developer experience improvements.
- Support troubleshooting and performance optimization across AI and streaming systems.
Required Experience
- Strong experience building and maintaining ML platforms or AI infrastructure in production environments.
- Hands-on experience with chat-based AI systems and conversational application architecture.
- Proven DevOps experience supporting streaming or real-time applications.
- Experience deploying and managing LLM/Generative AI infrastructure at scale.
- Strong understanding of distributed systems, containerization, and orchestration technologies.
- Experience with CI/CD pipelines, infrastructure automation, and cloud-native environments.
- Familiarity with vector databases, RAG architectures, embeddings, and inference optimization.
- Experience with observability, monitoring, logging, and platform reliability engineering.
• Kubernetes, Docker, Terraform
- Kafka, Pulsar, Flink, or Spark Streaming
- Python, Go, or similar backend languages
- ML frameworks such as PyTorch or TensorFlow
- LLM serving frameworks such as vLLM, TGI, or Ray Serve
- Vector databases such as Pinecone, Weaviate, Milvus, or Chroma
- Cloud platforms including AWS, GCP, or Azure
- CI/CD tools such as GitHub Actions, GitLab CI, or Jenkins
- Monitoring tools such as Prometheus, Grafana, OpenTelemetry, or ELK Stack
What Success Looks Like
- Reliable and scalable AI infrastructure supporting production-grade ML and LLM applications.
- Efficient deployment and monitoring of conversational AI systems and streaming workloads.
- Strong platform reliability, observability, and operational automation.
- Improved developer productivity and accelerated AI deployment lifecycle.
- Robust, secure, and cost-efficient infrastructure supporting rapid innovation.
Ideal Candidate Profile
- Strong engineering mindset with a passion for scalable AI systems.
- Comfortable operating in fast-paced, high-growth technology environments.
- Deep curiosity around emerging AI infrastructure and LLM tooling ecosystems.
- Excellent problem-solving and cross-functional collaboration skills.
- Ability to balance platform scalability, performance, reliability, and developer usability.
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