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Duration: Permanent/Direct Hire Compensation: $150-175k Base + Bonus + Benefits Location: Fort Collins, CO (on-site) Remote Eligibility: CA, DC, FL, GA, IL, IN, MN, MS, NC, NV, NY, OH, OR, PA, SC, TN, TX, VA, and WA Responsibilities: Build reusable platform services and frameworks for production AI applications. Establish common patterns for LLMs, RAG, AI agents, and machine learning services. Develop shared APIs, SDKs, libraries, templates, and internal tooling. Build platform capabilities for agentic workflows, including tool/function calling, orchestration, state/context management, and human-in-the-loop approvals. Develop reusable RAG capabilities including ingestion, chunking, embeddings, retrieval, ranking, and grounding. Build AI evaluation, guardrails, monitoring, and observability. Support model serving, inference services, vector/search infrastructure, and AI data pipelines. Build CI/CD and deployment patterns for AI applications. Establish LLMOps/MLOps practices for versioning, testing, deployment, monitoring, and rollback. Create self-service tooling and paved paths that allow engineering teams to consume AI platform capabilities independently. Design and operate secure, scalable AWS infrastructure for AI workloads. Partner with AI Engineers, Software Engineers, Data Engineers, Data Scientists, Security, SRE, and Product teams. Help establish architecture and engineering standards for production AI. Requirements: 5+ years of software engineering, platform engineering, SRE, DevOps, or cloud infrastructure experience. Strong hands-on AWS experience. Strong Python development experience. Hands-on experience building or supporting production Generative AI / LLM applications. Experience designing and implementing RAG solutions. Experience with embeddings, vector search/vector databases, retrieval, and grounding. Experience with AI agents / agentic workflows, including tool or function calling. Experience with LLMOps/MLOps practices. Experience implementing AI evaluation, guardrails, and observability. Hands-on experience with Kubernetes, containers, and Terraform/IaC. Experience building APIs, services, shared frameworks, or platform capabilities used by multiple engineering teams. Strong understanding of distributed systems, reliability, monitoring, and production operations. Experience working with sensitive or regulated data. Nice to Have: Experience with multiple LLM providers and model-routing/model-gateway architectures. Experience with AI orchestration or agent frameworks. Experience with vector databases and enterprise search platforms. Experience with event-driven or real-time architectures. Experience building internal developer platforms or self-service engineering tooling. Experience with fraud, risk, reconciliation, or financial workflow use cases. Fintech, banking, payments, or other regulated-industry experience. Experience with model serving and inference infrastructure. Experience optimizing AI systems for latency, scalability, and cost. 26-00881
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.