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About This Role
Job TypeFull Time LocationRemote Experience6–7 years of relevant IT experience, including leadership of IT operations and project delivery. Posted On01-10-2026
Qualifications
B.E. / B.Tech. / MCA
Job Description
Role Overview:
We are looking for a hands-on IT Manager to lead our IT delivery capabilities, ensure reliable technology services, and drive improvements through automation and AI.
The ideal candidate will combine strong operational ownership with the ability to deliver technology projects from requirements gathering through implementation and ongoing support. They must have demonstrable experience delivering AI-led projects and be ready to apply those capabilities to practical business and IT challenges.
Key Responsibilities:
- IT leadership and service delivery + Own end-to-end delivery of IT services across end-user support, infrastructure, networks, enterprise applications, and cloud platforms. + Establish service standards, escalation processes, and performance measures to ensure timely resolution of incidents and service requests. + Lead IT teams and service providers, assigning clear responsibilities and developing team capabilities. + Manage major incidents, stakeholder communication, service restoration, and corrective actions. + Maintain an IT improvement roadmap aligned with business priorities, growth plans, and operational requirements. + Implement and improve incident, problem, change, asset, and knowledge management processes.
- Infrastructure and project delivery + Plan and deliver IT projects, including cloud migrations, workplace technology deployments, infrastructure upgrades, and business application rollouts. + Own project scope, schedules, resources, budgets, dependencies, risks, and stakeholder reporting. + Ensure appropriate testing, business acceptance, documentation, training, and operational handover before project closure. + Oversee identity administration, endpoint management, software deployment, patching, connectivity, backup, and recovery. + Maintain accurate asset inventories, licensing records, configuration documentation, and lifecycle plans. + Manage technology vendors, procurement, contracts, renewals, and service performance. + Partner with the security function to implement required controls and resolve identified gaps. + Establish and test recovery procedures for critical IT services.
- AI-led projects and automation + Identify and prioritize opportunities where AI can improve IT delivery, employee productivity, or business processes. + Lead AI projects from problem definition and proof of concept through integration, rollout, adoption, and ongoing support. + Use Codex or comparable AI coding tools and agent frameworks to develop and validate practical automations and integrations. + Connect AI workflows with service management platforms, enterprise applications, APIs, and approved knowledge sources. + Establish appropriate data access, permissions, testing, human approvals, logging, and fallback procedures. + Measure outcomes such as reduced resolution time, lower manual effort, improved service quality, adoption, and operating cost.
Required Skills & Experience:
- 6–7 years of relevant IT experience, including demonstrated ownership of IT service delivery and technology projects.
- Proven experience leading IT teams, coordinating vendors, and managing business stakeholders.
- Experience supporting an enterprise environment; exposure to organizations with 500 or more users is preferred.
- Strong working knowledge of enterprise productivity platforms, cloud infrastructure, networking, identity administration, and endpoint management.
- Practical experience with IT service management tools, service-level agreements, change management, and operational reporting.
- Ability to manage multiple projects and operational priorities with clear accountability for delivery.
- Working capability in PowerShell, Python, or Bash, with an understanding of APIs, integrations, Git, and automation.
- Demonstrable hands-on contribution to AI-led projects, including at least one working implementation or pilot with real users.
- Ability to review, troubleshoot, and validate AI-generated scripts and workflows.
- Clear communication skills and the ability to explain technology decisions, costs, risks, and benefits to non-technical stakeholders.
Examples of Relevant AI Projects :
Candidates should be able to demonstrate one or more use cases such as:
- IT service desk assistance: Classify tickets, retrieve relevant knowledge, suggest resolutions, and route unresolved issues.
- Employee onboarding and offboarding: Coordinate approved account, access, device, and licensing tasks across systems.
- Internal knowledge assistants: Help employees find IT guidance while respecting document access permissions.
- Infrastructure operations: Analyze monitoring events and logs to support diagnosis and recommend corrective actions.
- Asset and licensing management: Identify unused licenses, inventory inconsistencies, and opportunities to reduce costs.
- Business workflow automation: Integrate applications to automate document processing, approvals, reporting, or repetitive administrative work.
Preferred Qualification:
- Experience with Microsoft 365, Azure, Intune, Entra ID, Google Workspace, AWS, or comparable platforms.
- Familiarity with workflow automation and enterprise AI platforms.
- Experience managing IT budgets, technology refreshes, and business continuity exercises.
- Relevant certifications such as ITIL, PMP, PRINCE2, or Microsoft, AWS, and networking certifications.
Practical Assessment:
Shortlisted candidates will present an IT delivery project and demonstrate an AI-enabled workflow using sanitized or synthetic data. They should explain their personal contribution, architecture, implementation decisions, testing, access controls, operational handover, and measurable results.
Success in This Role:
Success will be measured through reliable IT services, timely project delivery, improved employee satisfaction, effective cost management, and AI implementations that deliver measurable operational value.
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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