AI/ML Engineer

Jobtailor; Colorado; On-site; USD 120,000 - 160,000; Full time

Job summary

Jobtailor is seeking a hands-on AI/ML Engineer to design and deploy ML models, agentic AI systems, and LLM-based applications. You will translate business challenges into scalable AI solutions aligned with defined success metrics, and develop RAG pipelines, embeddings, and vector search architectures.

Collaborate with engineers and product stakeholders to deliver high-impact AI solutions in a fast-paced startup-like environment, focusing on performance, cost, and reliability.

Qualifications

• 2+ years of relevant experience required.

Responsibilities

• Design and deploy machine learning models, agentic AI systems, and LLM-based applications.
• Translate business challenges into scalable AI solutions aligned with success metrics.
• Develop RAG pipelines, embeddings, and vector search architectures.
• Build agentic workflows, prompt strategies, and orchestration patterns.
• Own AI/ML solutions end to end from design through deployment and operation.
• Evaluate model and agent performance using automated and human-in-the-loop methods.
• Optimize AI systems for latency, cost, scalability, and reliability.
• Support deployment workflows, CI/CD pipelines, containerization, and MLOps practices.
• Design and maintain data, feature, and inference pipelines with validation, lineage, monitoring, and reproducibility.

Skills

Python Programming

ML Deployment

LLM Application Development

MLOps Practices

CI/CD Pipeline Management

Vector Search Architectures

Agentic AI Systems

Embedding Strategies

Feature Engineering

Model Training

Evaluation Methodologies

Data Pipeline Design

Education

Advanced degree in Computer Science or related field

2+ years of relevant experience

Tools

FastAPI

MLflow

Docker

Cloud Platforms

LangChain

LlamaIndex

RAG Systems

CI/CD Tools

Vector Databases

Prompt-Based Workflows

Job description

Design and deploy machine learning models, Agentic AI systems, and LLM-based applications
Translate business challenges into scalable AI solutions aligned with defined success metrics
Develop RAG pipelines, embedding strategies, and vector search architectures
Build agentic workflows, prompt strategies, and orchestration patterns
Own AI/ML solutions end to end from design through deployment and operationalization
Evaluate model and Agent performance using automated and human-in-the-loop methods
Optimize AI systems for latency, cost, scalability, and reliability
Support deployment workflows, CI/CD pipelines, containerization, and MLOps practices to enable scalable and reliable AI system delivery
Design and maintain reliable data, feature, and inference pipelines with a focus on validation, lineage, monitoring, and reproducibility
Stay current with advancements in AI/ML, including LLMs, agentic systems, tooling, and applied best practices, and incorporate relevant innovations into team solutions
Collaborate with engineers, data scientists, product stakeholders, and platform teams to deliver scalable, high-impact AI solutions aligned with business and client needs.

Requirements

• Relevant degree preferred
• Advanced degree in Computer Science, Engineering, Data Science or a related field preferred
• 2 or more years of relevant experience required
• Experience deploying machine learning or AI applications into production environments required
• Strong Python expertise and software engineering practices required
• Experience building LLM applications such as RAG systems, prompt-based workflows, tool usage, and agentic AI systems preferred
• Hands-on experience with LLM frameworks, vector databases, embeddings, rerankers, LangChain, LlamaIndex, or similar technologies preferred
• Understanding of classical machine learning workflows, including feature engineering, model training, evaluation, error analysis, and monitoring
• Familiarity with tools and technologies such as FastAPI, MLflow, Docker, cloud platforms, and CI/CD pipelines preferred
• Strong understanding of evaluation methodologies for predictive ML and agentic AI-based systems preferred
• Demonstrated curiosity, initiative, and ability to quickly learn, evaluate, and apply emerging AI tools and technologies
• Experience working in startup, high-growth, or fast-paced product environments preferred.

Core Competencies

Demonstrates expertise in designing and deploying machine learning models and AI systems, with a strong focus on LLM applications and scalable AI solutions. Proficient in Python and MLOps practices, ensuring reliable and efficient delivery of AI systems.

Highest-signal resume keywords

• Machine Learning Model Deployment
• Python Programming
• LLM Application Development
• MLOps Practices
• CI/CD Pipeline Management

ATS Optimization Keywords

Hard Skills

• Machine Learning
• AI Application Development
• Feature Engineering
• Model Training
• Error Analysis
• Evaluation Methodologies
• Data Pipeline Design
• Vector Search Architectures
• Agentic AI Systems
• Embedding Strategies

Soft Skills

• Curiosity
• Initiative
• Collaboration

Industry Keywords

• AI Solutions
• Scalable Systems
• High-Growth Environments
• Startup Experience
• Predictive ML

Tools & Technologies

• FastAPI
• MLflow
• Docker
• Cloud Platforms
• LangChain
• LlamaIndex
• RAG Systems
• CI/CD Tools
• Vector Databases
• Prompt-Based Workflows