Interested in this AI/ML Engineer role at Sbt Global?
Apply Now →Skills & Technologies
About This Role
Company Description
Pay rate: ~$7,699.43/m, DOE
Work Location: Plano, TX
Contract Period: 1 yr
*Korean Bilingual Required*
Job Description
We are seeking a motivated Junior Generative AI & Agentic AI Developer to join our team in Plano, TX. This role focuses on designing, developing, and supporting AI-powered enterprise applications using Generative AI, Large Language Models (LLMs), and Agentic AI technologies.
The ideal candidate will have hands-on experience or strong interest in building AI applications using LLM frameworks, Retrieval-Augmented Generation (RAG), AI agents, prompt engineering, and cloud-based AI services. The candidate will collaborate with application developers, business stakeholders, and AI engineers to deliver intelligent automation solutions that improve enterprise productivity and business processes.
This position is ideal for a fast learner who is passionate about emerging AI technologies and wants to grow expertise in Generative AI application development, AI agents, and enterprise AI solutions.
Key Responsibilities
- Design, develop, and maintain Generative AI applications using Python and modern AI frameworks.
- Develop AI-powered enterprise solutions leveraging Large Language Models (LLMs), including OpenAI, Azure OpenAI, Amazon Bedrock, or similar AI platforms.
- Build Retrieval-Augmented Generation (RAG) solutions using enterprise data sources, vector databases, embeddings, and knowledge retrieval techniques.
- Design and develop Agentic AI workflows that enable autonomous task execution, reasoning, planning, and tool utilization.
- Develop AI agents using frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or similar agent orchestration technologies.
- Perform prompt engineering and optimize LLM responses for accuracy, reliability, and business use cases.
- Integrate Generative AI capabilities into existing enterprise applications through REST APIs and backend services.
- Develop AI-powered chatbots, copilots, and intelligent automation solutions.
- Support integration of AI models with business systems, databases, and enterprise platforms.
- Design and implement API services using Python frameworks such as FastAPI or Flask.
- Develop data processing pipelines for AI applications, including data preparation, transformation, and indexing.
- Work with relational databases and vector databases to support AI knowledge management solutions.
- Support AI model evaluation, testing, monitoring, and continuous improvement.
- Assist with deployment and operation of AI applications in cloud environments.
- Participate in MLOps/LLMOps activities including CI/CD automation, model lifecycle management, and application monitoring.
- Collaborate with application developers, DevOps engineers, and business teams to deliver AI-driven solutions.
- Troubleshoot application issues and improve system performance and reliability.
- Document AI solutions, architecture, development processes, and operational procedures.
- Participate in Agile/Scrum ceremonies, sprint planning, and technical discussions.
Qualifications
- Bachelor's degree in Computer Science, Engineering, Data Science, or related field, or equivalent professional experience.
- Working knowledge of Python programming language.
- Understanding of Generative AI concepts, Large Language Models (LLMs), and AI application development.
- Experience or academic/project exposure with LLM-based application development.
- Understanding of Retrieval-Augmented Generation (RAG) architecture and AI knowledge retrieval concepts.
- Familiarity with prompt engineering techniques and LLM response optimization.
- Basic understanding of AI Agent concepts, including workflow automation, reasoning, and tool integration.
- Experience with REST API development and integration.
- Understanding of database concepts including SQL, data modeling, and data processing.
- Familiarity with cloud platforms such as AWS, Azure, or Google Cloud.
- Exposure to AI/ML frameworks and libraries such as LangChain, PyTorch, TensorFlow, or similar technologies.
- Basic knowledge of software development practices including Git, version control, and code reviews.
- Understanding of DevOps concepts including CI/CD pipelines and containerization (Docker preferred).
- Experience working with Linux environments.
- Strong problem-solving skills and ability to learn emerging technologies quickly.
- Excellent verbal and written communication skills.
Performance Expectations
- Develop and deploy enterprise Generative AI applications that improve business productivity.
- Build reliable AI Agent workflows capable of automating business processes.
- Design effective RAG solutions using enterprise knowledge sources.
- Improve LLM application accuracy through prompt engineering and evaluation.
- Create scalable AI application architectures and reusable AI components.
- Collaborate effectively with business users and technical teams to deliver AI solutions.
- Continuously learn and apply emerging AI technologies to enterprise use cases.
Preferred Qualifications
- Experience developing enterprise Generative AI applications or AI automation solutions.
- Hands-on experience with Agentic AI frameworks such as LangGraph, AutoGen, CrewAI, or similar technologies.
- Experience with cloud AI services such as:
- Amazon Bedrock
- Azure OpenAI Service
- AWS SageMaker
- Azure AI Studio
- Experience with vector databases such as:
- FAISS
- ChromaDB
- Pinecone
- OpenSearch Vector Engine
- Experience building AI-powered chatbots, copilots, or intelligent assistants.
- Knowledge of Java/Spring Boot enterprise application development.
- Experience with enterprise system integration and application operations.
- English-Korean bilingual communication skills.
Additional Information
All your information will be kept confidential according to EEO guidelines.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Sbt Global, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $110,408.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Sbt Global AI Hiring
Sbt Global has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Plano, TX, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.