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About This Role
AI Engineer
Location: Charlotte, NC\- Hybrid
Job Description:
Need Stong Java \+ Python exp\- Both Java and Python is must along with AI
Key High\-Level Skills/Experience:
- Seasoned developer (10 plus years) with advanced experience in Java/Python
- Hands on AI experience developing LLM based applications
- Orchestration using Langraph/Google ADK
- MCP experience
About this role:
- Client is seeking a Lead AI Engineer with deep expertise in Python and Google ADK to join our TCOO Organization and be part of the Wholesale Operations Technology (WOT) team.
- In this role as an AI engineer, candidates will develop and lead cutting edge artificial intelligence solutions using large language models, agentic frameworks, and other innovative technologies.
- The ideal candidate would have experience in designing, developing, and deploying applications leveraging LLM models and systems, with a strong focus on Prompt Engineering, Fine Tuning, RAG implementation, and Agentic frameworks.
In this role, candidates will:
- Design, develop, deploy AI applications using LLM//'s, agent frameworks, and other related technologies
- Collaborate with enterprise teams to integrate LLM models with the existing WOT products and systems
- Develop and maintain large scale applications using Java, Python, OpenShift containers, and other relevant technologies
- Stay up to date with the latest advancements in AI, LLM, agentic frameworks and apply this knowledge to improve existing systems and develop new ones
- Troubleshoot and resolve complex technical issues related to applications and models
- Ensure systems are monitored to increase operational efficiency and managed to mitigate risk
- Lead, design, develop, test, and implement applications and system components, tools and utilities, models, simulation, model drift, and analytics to manage complex business functions using sophisticated technologies
- Resolve coding, testing and escalated platform issues of a technically challenging nature
- Lead team to ensure compliance and risk management requirements for supported area are met and work with other stakeholders to implement key risk initiatives
- Collaborate and influence all levels of professionals including managers
- Mentor junior software engineers and collaborate with other leads on the implementation of AI solutions
Required Qualifications:
- 10 plus years of Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
- 10 plus years of experience in software development
- 5 plus years of Python, Java, OpenShift containers, and other relevant technologies
- 3 plus years of Hands?on experience designing and deploying LLM?based applications
- 3 plus years of data engineering
- 3 plus years of AI and ML concepts, including deep learning, natural language processing and computer vision
- 2 plus years of Experience with SDLC and Agile tools such as JIRA, GitHub, Jenkins, Confluence etc.
Desired Qualifications:
- Bachelor’s or master’s degree in computer science, artificial intelligence or a related field
- Strong experience with Google Agent Development Kit (ADK) for building production?grade AI agents
- Knowledge of Chatbots, Copilot, GPT\-4 is preferred
- Advanced Prompt Engineering techniques (instruction design, few?shot, structured outputs)
- Retrieval Augmented Generation (RAG) design and implementation (vector stores, embeddings, chunking strategies)
- Experience monitoring model behavior, drift, accuracy, and reliability in production system
- Context management, memory handling, and guardrails for safe enterprise usage
- Experience with other programming languages such as JavaScript and React
- Certification in AI, machine learning or a related field
- Excellent problem\-solving skills come up with the ability to troubleshoot and resolve complex technical issues
- Strong collaboration and communication skills come with the ability to work effectively with cross functional teams
Regards,
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_
Manikandan Ravi (Mani)
Account Manager
Work: 972\-474\-8787 Ext: 424 \|Dir: 972\-214\-2415 \| [email protected]\| Themesoft Inc \|
Salary Context
This $124K-$135K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At THEMESOFT, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($130K) sits 28% below the category median. Disclosed range: $124K to $135K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
THEMESOFT AI Hiring
THEMESOFT has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Charlotte, NC, US. Compensation range: $135K - $135K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
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