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About This Role
AI Dev Lead – LLM \& Applied AI Solutions
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EXPERIENCE REQUIRED: 2\+ Years
NUMBER OF POSITIONS: 7
DEPARTMENT: Information Systems
REPORTS TO: IS Head
LOCATION: Dallas Fort Worth, USA \& Vadodra or Gurugram, India
ROLE OVERVIEW:
We are seeking an experienced AI dev lead who has a strong foundation in modern AI technologies and hands\-on experience wrapping and deploying LLMs for real\-world applications. The ideal candidate understands both AI product architecture and software engineering, with the ability to design, integrate, and optimize intelligent systems using APIs, vector databases, and enterprise data pipelines.
This individual will collaborate with project managers, solution architects, and developers to build and maintain scalable AI\-enabled web applications, while also managing the infrastructure and data layers that make these tools reliable and secure.
ESSENTIAL DUTIES AND RESPONSIBILITIES:—
Design, build, and deploy solutions that wrap large language models (OpenAI, Anthropic, Claude, Gemini, etc.) for practical business use cases.—
Implement and optimize retrieval\-augmented generation (RAG) frameworks using vector databases (e.g., Quadrant, Pinecone, FAISS, Chroma, Milvus).
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Build robust API integrations between AI providers, internal systems, and user interfaces.—
Structure and maintain SQL or NoSQL databases to support knowledge retrieval, model context, and user interaction histories.—
Collaborate with front\-end engineers to design user\-friendly interfaces for AI chatbots, dashboards, and knowledge assistants.—
Conduct prompt engineering, performance evaluation, and continuous tuning of model responses to improve accuracy and reliability.
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Manage corpus ingestion, vectorization, and updating of proprietary datasets feeding the AI tools.—
Ensure compliance with data governance, security, and privacy best practices related to AI and sensitive data.—
Document architectural decisions, workflows, and model performance. Support agile sprints and cross\-team communication.
SUPERVISORY RESPONSIBILITIES:
This position does not have supervisory responsibilities.
LANGUAGE REQUIREMENTSRequired English Ability Level Business Fluent
Required Hindi Ability Level Business Fluent
SKILLS:
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2\-3 years of professional experience in AI development, with at least one end\-to\-end deployed LLM or AI product.
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Strong programming background in Python (preferred) or C\#.
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Experience with LLM APIs (OpenAI, Azure OpenAI, Anthropic, Hugging Face, etc.).
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Solid understanding of RAG architecture and vector database design.
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Experience with RESTful API development and integration.
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Proficiency in SQL (T\-SQL, PostgreSQL, MySQL) and general database design.
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Familiarity with LangChain, LlamaIndex, or similar AI orchestration frameworks.
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Working knowledge of cloud platforms (Azure, AWS, or GCP) for hosting AI workloads.
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Understanding of Docker, Kubernetes, or similar container orchestration tools.
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Experience integrating AI with web front ends (React, Next.js, or Streamlit preferred).
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Knowledge of embedding models, prompt engineering, and token optimization.
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Exposure to data transformation pipelines and ETL tools.
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AI/ML certification from a recognized institution (Microsoft, Google, Coursera, etc.).
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Experience working with Git, CI/CD pipelines, and DevOps environments.
QUALIFICATIONS:
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Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Engineering, or related field
LANGUAGE SKILLS:
Ability to read, analyze and interpret the most complex documents. Ability to respond effectively to the most sensitive inquiries or complaints. Ability to write emails, speeches and articles using original or innovative techniques or style. Ability to make effective and persuasive speeches and presentations on controversial or complex topics to top management, public groups and clients.
MATHEMATICAL SKILLS:
Ability to choose the right mathematical methods or formulas to solve a problem. Ability to add, subtract, multiply, and divide in all units of measure, using whole numbers, common fractions, and decimals quickly and correctly.
REASONING ABILITY:
Ability to define problems, collect data, establish facts, and draw valid conclusions. Ability to interpret an extensive variety of technical instructions in mathematical or diagram form and deal with several abstract and concrete variables.
CERTIFICATES, LICENSES, REGISTRATION:
Master’s degree in Computer Science, Artificial Intelligence, Data Engineering, or related field
PHYSICAL DEMANDS:
The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
While performing the duties of this job, the employee is regularly required to sit, stand, and walk. Hearing and speaking to exchange information in person and on the phone. Seeing to read and write, exchange emails, conduct work, and prepare documents and reports. Minimal to light physical effort is generally required in performing duties in an office environment. This position requires the ability to operate a computer keyboard and standard office equipment at efficient speed. The employee frequently is required to reach with hands and arms and stoop, kneel, crouch or crawl. The employee is occasionally required to climb or balance. The employees must occasionally be required to lift and/or move up to 10 pounds and occasionally lift and/or move up to 25 pounds. Specific vision abilities required by this job include close vision, distance vision, peripheral vision, depth perception and ability to adjust focus.
WORK ENVIRONMENT:
The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
While performing the duties of this job, the noise level in the work environment is usually quiet to moderate.
DISCLAIMER:
The information in this job description is designed to indicate the general nature and level of work performed by employees within this classification. It is not designed to contain or be interpreted as a comprehensive inventory of all duties, responsibilities and qualifications required of employees assigned to this position and may be changed at the company’s discretion to conform to business needs.
ABOUT THE COMPANY:
AIS is a Texas\-based fintech firm committed to lowering operating costs, improving quality and reducing cycle time with back\-office automation, highly skilled talent and standardized reporting and analytics solutions. AIS manages the day\-to\-day work so our clients can focus on growing their business. We review client processes, eliminate non\-value adds, and enhance productivity. We build financial and legal technology to automate and optimize workforce performance. We recruit, train, and manage specialized human resources to meet staff augmentation needs. We equip decision makers with deep data sets and forward\-thinking analytics so they can make smarter business decisions and create better customer experiences. We serve a variety of industries including banking, automotive finance, credit card, mortgage, insurance and telecommunications.
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At AIS InfoSource, 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
AIS InfoSource AI Hiring
AIS InfoSource has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas-Fort Worth, TX, US.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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