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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI/ML Engineer - **Company:** Tempo Software - **Location:** United States (Remote available) - **Experience:** Expert - **Salary:** $160,000.0 - $200,000.0 - **Contract:** Permanent contract - **Skills:** A/B Testing, Artificial Intelligence, Amazon Web Services, Audit Trail, Microsoft Azure, Big Data, Cloud Computing, Code Review, Databases, Continuous Integration, Python (Programming Language), Machine Learning, Monte Carlo Methods, Open Source Technology, Pattern Recognition, Productivity Software, Recommender Systems, OpenAI, Signal Processing, Software Engineering, Data Streaming, TypeScript, LangChain, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Google Vertex AI, Llamaindex, Generative AI, Backend, Kotlin, Build Management, AI Platforms, Information Technology, Optimization Algorithms, CrewAI, AutoGen, Machine Learning Operations, Claude, Model Context Protocol, GPT - **Published:** October 17, 2026 - **Apply:** https://www.builtincolorado.com/auth/login?destination=/job/senior-ai-ml-engineer/11729156 ## About the Role * A track record of shipping LLM-powered features or products - prototypes don't count; we want to see things that real users have relied on. * Hands-on experience orchestrating agents - multi-step reasoning, tool use, autonomous action with guardrails. Frameworks like LangChain, LlamaIndex, CrewAI, AutoGen, or equivalent (including rolling your own). * Deep LLM engineering fundamentals: prompt engineering, RAG architectures, function calling / tool use, context management, evaluation-driven development. * Production-quality engineering practices - you write code with tests, participate in code review, care about CI/CD and observability. You build systems that run reliably in production, not notebook prototypes. * Experience with event-driven or streaming data systems - CDC events, real-time pipelines, and the patterns that come with them. * 5+ years in software engineering, with 3+ years focused on AI/ML in production systems. * Ability to work embedded in a product team - collaborating daily with domain engineers, product managers, and designers, not just other AI specialists. Strongly Preferred * AWS Bedrock, Azure OpenAI, or GCP Vertex AI - we're running on Bedrock with Claude today. * MCP (Model Context Protocol) or similar tool-use / agentic frameworks. * Anomaly detection, time-series analysis, or statistical signal processing. * Building confidence scoring or calibration systems for AI outputs. * A track record of absorbing work from external partners and making it your own - you know how to inherit an architecture, evaluate its decisions, and improve what you find. * Proficiency in Kotlin or TypeScript alongside Python - our product platform is Kotlin/TypeScript; our AI platform is Python. You'll work across both. Nice to Have * Monte Carlo simulation, optimization algorithms, or scheduling systems. * Background in portfolio management, project management, or resource planning. * Experience in enterprise SaaS - multi-tenancy, compliance, audit trails. * Contributions to open-source AI/ML projects. Mindset * You use AI tools heavily in your own daily work - not as a novelty, but because they make you meaningfully more effective. * You have strong opinions (loosely held) about what makes AI products trustworthy versus impressive-but-fragile. * You're energized by a space that's moving fast, and you track model releases, emerging agent frameworks, and new patterns as they happen. * You're as comfortable writing an Architecture Decision Record as you are writing a prompt. ## Description We're looking for a Senior AI/ML Engineer who will be working at the intersection of LLMs, real-time signal processing, and enterprise decision-making. This is not a research role or an isolated AI team position. You'll sit alongside domain engineers as you ship production AI systems to enterprise customers. This is not the typical AI/ML engineering role. We're looking for someone who uses AI heavily in their own daily work, has built and shipped features powered by LLMs and autonomous agents, and has strong opinions on what good AI product engineering actually looks like. If your go-to move when facing a complex problem is to reach for an agent pipeline rather than a static script, you're the kind of engineer we have in mind. You'll initially pair with our external AI partners to absorb their work and establish the patterns the broader engineering team will follow. Over time, you become the internal anchor for AI engineering - the person the rest turn to for prompt design, evaluation strategy, model selection, and agent architecture decisions. What you'll do: Signal detection and anomaly detection Statistical and ML-based detectors that identify meaningful patterns in portfolio signals - velocity changes, capacity saturation, dependency risks - from CDC event streams and external tool integrations. Not simple threshold alerts; intelligent pattern recognition that knows the difference between noise and a real problem. Insight synthesis engine An LLM-powered correlation engine that takes raw signals and produces actionable insights with root causes, confidence scores, and evidence chains. Not just "something is wrong" - but "why it's wrong, what it means, and what you should do about it." Planning Rules compiler A translation layer between natural language planning rules (written by portfolio managers) and the structured parameters that drive our Monte Carlo scheduling engine. The LLM interprets intent; the deterministic engine computes schedules. You'll design how these two layers communicate reliably. Evaluation and testing frameworks The pipelines that ensure AI outputs are reliable, consistent, and improving over time. Regression suites for prompt changes, A/B testing infrastructure for model updates, confidence calibration - because vibes-based testing doesn't scale at enterprise scale. MCP tool definitions LLM-ready tool specs for domain capabilities (Item Store queries, capacity lookups, scenario simulations) that Tempo AI can discover and invoke at runtime within a hub-and-spoke MCP architecture already in production., * Impact: Work on meaningful products that empower enterprise users and improve productivity. * Innovation: Be part of a culture that values creativity and innovation, with opportunities to make a real impact. * Collaboration: Join a supportive, collaborative UX team that values openness, communication, and a continuous learning environment. * Growth: Opportunities for professional development, including conferences, courses, and mentorship. What's In It For You (Org-wide) * Remote First work environment * Unlimited vacation in most of our locations!! * Great benefits including health, dental, vision and savings plan. * Perks such as training reimbursement, WFH reimbursement, and more. * Diverse and dynamic teams with challenging and exciting work. * An opportunity to have a real impact on our business. * A great range of social activities (both in person and virtual). * Optional in person meet-ups and the ability to travel to our international offices * Employee referral program * And so much more!, Lead the evaluation, training, fine-tuning, and production integration of machine learning and large language models. Build prompt engineering and model evaluation pipelines, optimize Elasticsearch-backed AI workflows, benchmark commercial and open-source models, and monitor production performance. Collaborate with product, engineering, and security teams to deliver safe, scalable AI capabilities while identifying improvements to data pipelines and engineering processes. Top Skills: Amazon BedrockAmazon SagemakerAnthropicElasticsearchHugging FaceMeta LlmsMistralOpenaiPythonPyTorchScikit-LearnTensorFlow Wonuva Artificial Intelligence Engineer 2 Days Ago Remote or Hybrid United States 160K-200K Annually Senior level 160K-200K Annually Senior level Artificial Intelligence * Information Technology * Generative AI * Big Data Analytics Design, build, deploy, and optimize scalable AI and machine learning systems using large datasets. Develop predictive models, recommendation systems, personalization engines, forecasting solutions, generative AI applications, and production ML pipelines. Collaborate with data, backend, and product teams to integrate models into applications, while monitoring performance, data quality, drift, reliability, and cost. Establish best practices for evaluation, experimentation, reproducibility, and deployment, and research emerging AI technologies. Top Skills: Ai AgentsAirflowSparkAPIsAWSCi/CdDatabricksDeep LearningDockerEmbeddingsGCPLarge Language ModelsAzureMlopsNumpyPandasPythonPyTorchRagRelational DatabasesScikit-LearnSQLTensorFlowTransformer ArchitecturesVector Databases Ciklum