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About This Role
Job TypeFull Time LocationRemote Experience5+ years Posted On01-10-2026
Qualifications
Bachelor’s or Master’s degree in Computer Science or a related field
Job Description
Role Overview:
We are seeking an experienced AI Engineer with 5+ years of experience in building AI/ML solutions and designing Generative AI applications for enterprise use cases. The ideal candidate should have hands-on experience developing scalable AI-powered solutions, including Retrieval-Augmented Generation (RAG) systems, intelligent assistants, AI agents, and workflow automation solutions. The candidate should possess strong expertise in Large Language Models (LLMs), prompt engineering, vector databases, and modern AI frameworks. Exposure to Clinical Research, Life Sciences, Healthcare, or Pharmaceutical domains will be considered a significant advantage. This role offers the opportunity to work on cutting-edge AI initiatives and contribute to the design and delivery of innovative solutions that drive business value.
Key Responsibilities:
- Design, develop, and deploy AI/ML and Generative AI solutions for enterprise applications.
- Build and optimize Retrieval-Augmented Generation (RAG) systems using vector databases, embeddings, and semantic search techniques.
- Develop AI-powered assistants, agents, and intelligent workflows using modern AI frameworks and orchestration tools.
- Collaborate with business stakeholders, product teams, and domain experts to understand requirements and translate them into scalable AI solutions.
- Integrate AI services with enterprise applications, APIs, databases, and cloud platforms.
- Evaluate, monitor, and improve model performance, accuracy, reliability, and user experience.
- Implement best practices related to AI architecture, model evaluation, governance, and deployment.
- Stay updated with emerging AI technologies and recommend innovative approaches to solve business challenges
Required Skills & Experience:
- 5+ years of experience in AI/ML, Data Science, or Generative AI development.
- Strong programming skills in Python and familiarity with software engineering best practices.
- Hands-on experience with Machine Learning, Natural Language Processing (NLP), and Large Language Models (LLMs).
- Experience designing and implementing Generative AI solutions and applications.
- Strong experience building and deploying RAG-based solutions.
- Experience with embeddings, vector databases, semantic search, prompt engineering, and model evaluation techniques.
- Familiarity with AI orchestration frameworks such as LangChain, LangGraph, CrewAI, or similar technologies.
- Experience developing and consuming REST APIs.
- Working knowledge of cloud platforms such as AWS, Azure, or GCP.
- Understanding of AI deployment, monitoring, and optimization practices.
- Experience with AI tools (Claude, Cursor, etc.
Professional Attributes:
- Strong analytical and problem-solving capabilities.
- Ability to translate business requirements into scalable AI solutions.
- Excellent communication skills.
- Ability to work independently as well as collaboratively within cross-functional teams.
- Strong learning mindset with a passion for emerging AI technologies and innovation.
- Ability to effectively manage multiple priorities in a fast-paced environment.
Experience & Qualification:
- Bachelor’s or Master’s degree in Computer Science or a related field.
- Minimum 5 years of relevant experience in AI/ML and Generative AI solution development.
- Exposure to Clinical Research, Healthcare, Pharmaceutical, or Life Sciences domains is highly desirable.
- Experience building AI agents, multi-agent systems, or enterprise AI solutions will be an added advantage.
- Experience building front-end applications using technologies such as React,
- Strong client-facing consulting experience, with experience leading PoCs Lead solution discovery workshops, requirement-gathering sessions, and technical consulting engagements with client stakeholders
- Own the complete product lifecycle design, develop, and deploy AI-powered products, accelerators, PoCs, and MVPs from concept to production
- Build production-quality software and intelligent applications using LLMs, RAG, AI Agents, MCP, vector databases, and workflow orchestration Angular, JavaScript, or TypeScript would be a plus.
- Full-stack development experience and the ability to build endto-end AI-powered applications will be considered an additional advantage.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At I2E Consulting Pvt Ltd, 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 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
I2E Consulting Pvt Ltd AI Hiring
I2E Consulting Pvt Ltd has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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