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About This Role
Job TypeFull Time
LocationRemote
ExperienceMinimum 3\-4 years of relevant experience in Full Stack Development.
Posted On01\-07\-2026
### Qualifications
Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field.### Job Description
Role Overview:
We are seeking an experienced Full Stack Developer with 3\-4\+ years of experience in building modern enterprise applications and digital solutions. The ideal candidate should possess strong expertise across front\-end and back\-end technologies and demonstrate proficiency in leveraging Al\-powered development methodologies to accelerate software delivery, improve quality, and drive innovation.
This role requires a developer who views Al as a core engineering capability rather than simply a coding assistant. The candidate should be capable of utilizing Al throughout the software development lifecycle, including requirement analysis, solution design, architecture generation, code development, testing, documentation, code reviews, debugging, optimization, and deployment automation.
The ideal candidate will have experience using modern Al development tools such as Claude, Cursor, GitHub Copilot, ChatGPT, Windsurf, or similar platforms to rapidly develop, maintain, and enhance enterprise\-grade applications. Exposure to Pharmaceutical, Life Sciences, Clinical Research, or Healthcare domains will be considered a significant advantage.
Key Responsibilities:
- Design, develop, test, and deploy enterprise\-grade full\-stack applications using modern development frameworks and cloud technologies.
- Utilize Al\-assisted development methodologies to improve productivity, code quality, and delivery timelines.
+ Leverage Al tools for:
+ Requirement analysis and technical solution design.
+ Architecture and component design recommendations.
+ Code generation and refactoring.
+ Unit test and integration test generation.
+ Automated documentation creation and maintenance.
+ Code review, debugging, and optimization.
+ CI/CD pipeline automation and deployment support.
- Build scalable front\-end applications using React, Angular, or similar frameworks.
- Develop secure and performant backend services, APIs, and microservices.
- Integrate enterprise applications with Al services, LLM APIs, workflow automation platforms, and intelligent assistants.
- Collaborate with business stakeholders and domain experts to translate requirements into scalable technical solutions.
- Apply prompt engineering techniques and Al workflow orchestration to maximize the effectiveness of Al\-assisted development.
- Ensure Al\-generated code adheres to enterprise coding standards, security requirements, architectural guidelines, and regulatory compliance standards.
- Contribute to the development of Al\-enabled products, intelligent workflows, automation solutions, and digital transformation initiatives.
- Stay updated on emerging Al engineering practices, agentic development workflows, and next\-generation software development methodologies.
Required Skills \& Experience:
- JavaScript / TypeScript
- HTML5 / CSS3
- Python, Node.js, .NET, or Java
- REST APIs and Microservices Architecture
- Experience working with SQL and NoSQL databases.
- Strong understanding of software architecture, design patterns, and scalable application development.
- Hands\-on experience with Al\-assisted software development tools such as:
+ GitHub Copilot,
+ Claude,
+ Cursor,
+ ChatGPT,
+ Windsurf,
+ Similar Al coding assistants
- Experience using Al for:
+ Code generation
+ Test automation
+ Documentation generation
+ Code review and optimization
+ Debugging and troubleshooting
+ Development workflow automation
- Understanding of Generative Al concepts, LLMs, Prompt Engineering, Al Agents, MCP (Model Context Protocol), RAG architectures, and Al integrations.
- Experience integrating Al APIs and intelligent automation capabilities into enterprise applications.
- Familiarity with cloud platforms such as AWS, Azure, or GCP.
- Experience with Docker, Kubernetes, CI/CD pipelines, and DevOps practices.
Professional Attributes:
- Experience building Al\-powered applications, intelligent assistants, Al agents, or workflow automation solutions.
- Experience working in Pharmaceutical, Clinical Research, Healthcare, or Life Sciences domains.
Professional Attributes:
- Strong analytical and problem\-solving skills.
- Ability to translate business requirements into scalable technical solutions.
- Excellent communication and stakeholder management skills.
- Ability to work independently and collaboratively within cross\-functional teams.
- Strong learning mindset with a passion for emerging technologies and Al innovation.
- Ability to effectively manage multiple priorities in a fast\-paced environment.
- Strong attention to detail and commitment to delivering high\-quality solutions.
Experience \& Qualification:
- Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field.
- Minimum 3\-4 years of relevant experience in Full Stack Development.
- Experience building enterprise\-grade web applications and APIs.
- Exposure to Clinical Research, Healthcare, Pharmaceutical, or Life Sciences domains is highly desirable.
- Experience integrating Al services, LLM APIs, intelligent assistants, or automation solutions will be an added advantage.
- Experience building cloud\-native applications and deploying solutions on AWS, Azure, or GCP will be preferred.
- Knowledge of Al\-assisted software engineering practices and modern development workflows will be considered a strong plus
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At I2E Consulting Pvt Ltd, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $219,250 based on 424 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
I2E Consulting Pvt Ltd AI Hiring
I2E Consulting Pvt Ltd has 1 open AI role right now. They're hiring across AI Software Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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.
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