Machine Learning Engineer (GoLang)

Make your mark at Comcast -- a Fortune 30 global media and technology company. From the connectivity and platforms we provide, to the content and experiences we create, we reach hundreds of millions of customers, viewers, and guests worldwide. Become part of our award-winning technology team that turns big ideas into cutting-edge products, platforms, and solutions that our customers love. We create space to innovate, and we recognize, reward, and invest in your ideas, while ensuring you can proudly bring your authentic self to the workplace. Join us. You’ll do the best work of your career right here at Comcast. (In most cases, Comcast prefers to have employees on-site collaborating unless the team has been designated as virtual due to the nature of their work. If a position is listed with both office locations and virtual offerings, Comcast may be willing to consider candidates who live greater than 100 miles from the office for the remote option.) Job Summary Multimodal Analysis Framework (MAF)** is an end‑to‑end platform designed to process diverse content sources—including **video, images, audio, and documents**—to generate rich, structured metadata. The platform unifies multiple ML/AI models to extract curated insights at scale, tailored to specific business needs. MAF supports both **on‑demand** workloads (batch uploads, ad‑hoc analysis) and **real‑time streaming** workflows, enabling continuous metadata generation for live content streams. Customers can define their metadata requirements—such as entity extraction, scene segmentation, object detection, transcription, summarization, or multimodal correlation—and the framework orchestrates the appropriate models and toolchains to deliver high‑quality outputs. Through flexible APIs and UI‑based workflows, customers and internal teams can visualize metadata, trigger enrichment, monitor processing, and integrate results into downstream applications. The platform emphasizes modularity, scalability, and extensibility to support new ML models, LLM‑based agents, and cross‑modal inference as use cases evolve. We are looking for a **mid-level Backend Engineer** to join our **Machine Learning Platform team**. This role focuses on building **scalable backend systems** that power ML workloads, including **video, image, and document processing**, and enable **LLM-driven applications** through **agents and MCP servers**. You will work primarily in **Golang**, deploy and operate services on **Kubernetes**, manage infrastructure with **Terraform**, and build on **AWS**. A core part of the role is designing platform capabilities that allow **LLMs to safely and reliably interact with tools, data, and services** via **agent frameworks and MCP servers**. Job Description Backend Engineering (Golang) Design, build, and maintain **high-performance backend services** in **Golang** for ML and AI platform use cases. Develop **REST and gRPC APIs** for inference, processing pipelines, orchestration, and platform services. Implement asynchronous and distributed processing patterns (workers, queues, event-driven systems). Ensure backend services meet production standards for **scalability, reliability, and security**. ML Platform & Processing Pipelines   Build and operate backend systems supporting: Video processing** (frame extraction, metadata generation, embeddings, indexing). Image processing** (OCR, classification, detection, embedding generation). Document processing** (parsing, layout analysis, chunking, OCR, retrieval pipelines). Integrate ML inference services into backend workflows with attention to **latency, throughput, and cost**. Work closely with ML engineers and data scientists to productionize models and pipelines. LLMs, Agents, and MCP Servers Build **LLM-enabled backend services** using structured prompting, tool/function calling, and retrieval-augmented generation (RAG). Design and implement **agentic workflows** (multi-step reasoning, tool orchestration, retries, guardrails). Develop and operate **MCP servers** that expose internal platform capabilities (search, retrieval, processing, data access) to LLM-based applications. Enforce **security, access control, and observability** for agent and MCP interactions. Vector Search & Retrieval   Design and maintain vector-based retrieval systems using **Milvus**. Implement embedding ingestion, indexing, and query pipelines at scale. Optimize retrieval quality, latency, and relevance for downstream LLM applications. Cloud, Kubernetes & Infrastructure   Deploy and operate backend and ML services on **Kubernetes** (scaling, rollouts, resource management). Use **Terraform** for infrastructure provisioning and continuous delivery of cloud resources. Build and operate primarily on **AWS**, leveraging services such as: Compute, networking, and IAM Object storage Managed Kubernetes Logging and monitoring services Reliability, Quality & Operations Implement observability using logs, metrics, and traces; define SLOs and alerts. Write automated tests (unit, integration) and contribute to CI/CD pipelines. Participate in on-call rotations and incident response; drive post-incident improvements. Required Qualifications   **3–6 years** of professional software engineering experience. Strong backend engineering experience with **Golang**. Experience building and operating **APIs** (REST and/or gRPC) in production. Hands-on experience with **Kubernetes** in production environments. Experience using **Terraform** for infrastructure provisioning and deployment. Solid working knowledge of **AWS** cloud services and core architectural concepts. Experience building or supporting **ML processing pipelines** (video, image, or document). Practical experience using **LLMs** in production systems. Experience developing **agents** and/or **MCP servers**, or equivalent tool-integration platforms. Preferred / Nice-to-Have Qualific