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About This Role
Job TypeFull Time LocationRemote ExperienceMinimum 5–6 years of relevant security leadership and hands-on delivery experience Posted On01-10-2026
Qualifications
Bachelor’s degree in Computer Science/IT/Cybersecurity or related field. Relevant security certifications are an advantage.
Job Description
Role Overview:
We are looking for a hands-on security professional who can lead our organizational security functions and apply agentic AI to practical cybersecurity challenges.
The ideal candidate will combine strong security operations experience with the ability to build, test, and demonstrate AI-assisted security workflows using OpenAI Codex or comparable agent harnesses and frameworks. Candidates must bring current implementation experience and be ready to contribute to agentic AI projects from the outset.
Key Responsibilities:
- Security leadership and operations + Own and improve day-to-day security operations across endpoints, identities, networks, cloud infrastructure, applications, and business data. + Maintain a prioritized security improvement roadmap based on business risks, operational needs, and audit findings. + Lead incident investigation, containment, recovery, root-cause analysis, and corrective actions. + Manage vulnerability identification, risk-based prioritization, remediation coordination, and closure verification. + Implement and improve controls involving SIEM, EDR/XDR, identity and access management, MFA, endpoint management, email security, and data loss prevention. + Maintain security policies, incident response playbooks, access review processes, and security awareness initiatives. + Coordinate with IT, engineering, business stakeholders, and external security providers to deliver measurable improvements. + Report security posture, significant risks, incidents, and remediation progress to management.
- Agentic AI and security automation + Identify security processes where AI agents can reduce manual effort, improve investigation quality, or accelerate remediation. + Build and maintain workflows using Codex or comparable tools that can work with code repositories, execute approved tools, interact with APIs, and produce verifiable outputs. + Integrate AI workflows with security platforms, ticketing systems, repositories, and internal knowledge sources. + Review and test AI-generated scripts, detections, findings, and remediation proposals before operational use. + Apply appropriate controls for sensitive data, credentials, tool permissions, execution environments, audit logging, and human approval of consequential actions. + Evaluate workflows for accuracy, false positives, reliability, cost, and measurable operational benefit.
Required Skills & Experience:
- At least 5–6 years of experience leading and delivering organizational security functions, including responsibility for an environment supporting 200 or more users.
- Demonstrated ownership of security outcomes, including incident handling, control implementation, risk reduction, and remediation follow-through.
- Strong working knowledge of endpoint security, identity security, network security, cloud security, and data protection.
- Hands-on experience with SIEM and EDR/XDR platforms, vulnerability management tools, and security investigations.
- Ability to write, understand, troubleshoot, and maintain Python, PowerShell, or Bash scripts.
- Practical experience with Git, APIs, structured data, and automation integrations.
- Current hands-on experience building agentic AI workflows using Codex or comparable agent harnesses. General chatbot usage or prompt-writing experience alone is insufficient.
- Ability to explain an agent’s tools, permissions, context, execution steps, failure modes, and validation approach.
- Strong communication skills, with the ability to translate technical findings into clear business risks and actions.
Examples of Relevant AI Projects:
Candidates should be able to demonstrate one or more practical use cases such as:
- Alert investigation: Enrich security alerts with relevant logs and asset context, then produce evidence-backed investigation summaries.
- Phishing analysis: Examine suspicious emails, extract indicators, correlate available evidence, and recommend response actions.
- Vulnerability management: Combine scanner findings with asset criticality and exposure to prioritize remediation and prepare tickets.
- Detection engineering: Develop and test detection queries or rules against representative security events.
- Application security: Investigate code vulnerabilities, validate findings in an isolated environment, and propose tested fixes for review.
- Security assurance: Collect control evidence, identify configuration gaps, and draft reports linked to supporting records.
Preferred Qualification:
- Experience securing Microsoft 365, Azure, AWS, Google Cloud, or comparable enterprise environments.
- Familiarity with ISO 27001, NIST CSF, CIS Controls, and MITRE ATT&CK.
- Experience with SOAR, CI/CD security, infrastructure as code, or container security.
- Familiarity with prompt injection, unsafe tool execution, excessive agent permissions, and sensitive-data exposure risks.
- Relevant certifications such as CISSP, CISM, GCIH, GCIA, Security+, or vendor security certifications.
Practical Assessment:
Shortlisted candidates will be asked to demonstrate a working AI-enabled security workflow using sanitized or synthetic data and explain:
- The security problem and their personal contribution.
- The architecture, integrations, and agent permissions.
- How outputs were tested and verified.
- How errors, sensitive information, and approval requirements are handled.
- The observed results, limitations, and operational value.
Success in This Role:
Success will be measured through improved security coverage, faster investigation and remediation, fewer recurring control gaps, and reliable AI workflows that deliver demonstrable benefits.
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 9,079 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
I2E Consulting Pvt Ltd AI Hiring
I2E Consulting Pvt Ltd has 3 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 $188,000 across 2,316 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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