Interested in this AI/ML Engineer role at Ciklum?
Apply Now →About This Role
Ciklum is looking for an Agentic Principal Lead Consultant to join our team full\-time in the US .
We are a custom product engineering company that supports both multinational organizations and scaling startups to solve their most complex business challenges. With a global team of over 4,000 highly skilled developers, consultants, analysts and product owners, we engineer technology that redefines industries and shapes the way people live.
About the role:
As an Agentic Principal Lead Consultant, become a part of a cross\-functional development team engineering experiences of tomorrow. Client is building an AI\-native operating model across every function (Go\-To\-Market, Tech, Finance, HR/Legal, and Services). The Internal AI Solution Architect is the first hire on the central AI Transformation team and the builder behind client’s AI Lab.
The role reports to the Head of AI Transformation and works directly across all internal functions to land AI in real workflows, not just in demos.
Responsibilities:
Partner with leaders across Finance/RevOps, Go\-To\-Market, Services, HR/Legal, and Engineering to identify the highest\-leverage opportunities to embed AI into real operating workflows (not one\-off demos)
Facilitate discovery: map current\-state processes, quantify pain points, define “good” outcomes, and translate ambiguous needs into clear workflow requirements and success metrics
Build and maintain an AI workflow roadmap sequenced by business impact, readiness, and change capacity; align priorities with function leaders and the AI Transformation team
Drive build\-versus\-buy assessments for each opportunity (SaaS tools, platforms, automation solutions), delivering a clear recommendation and implementation approach
Lead change management and enablement: run working sessions, produce playbooks, host trainings/brown bags, and coach teams to raise AI literacy and sustained adoption
Create reusable patterns and standards for “AI in internal tools” (workflow templates, governance checklists, measurement frameworks) so other teams can move faster
Establish operational guardrails for AI\-enabled workflows in partnership with Security, Legal, and Compliance (data handling, PII safety, access controls, prompt injection resilience, human\-in\-the\-loop requirements)
Define evaluation and monitoring expectations for workflows (accuracy/quality, safety, cost, latency, adoption); implement feedback loops so workflows improve over time
Requirements:
6\+ years of experience in internal consulting, business operations, business systems, product operations, solutions architecture, or an equivalent role that sits at the intersection of business and technology
Must have software\-engineering experience, including 4\+ years shipping internal AI\-native tools to production
Must have experience standing up an internal AI program, workflow automation COE, or “AI for employees” capability at a prior company
Experience in B2B SaaS, enterprise workflows, and/or regulated or high\-compliance environments (privacy, security, auditability)
Strong stakeholder management: comfortable influencing across functions, aligning senior leaders, and driving decisions amid competing priorities
Ability to translate business problems into structured requirements, operating metrics, and phased delivery plans
AI fluency: understanding of how LLM\-based systems work (prompting, retrieval, tool use/function calling at a conceptual level) and how to evaluate quality, risk, and cost/latency trade\-offs
Experience evaluating and implementing software solutions (build vs buy), including vendor assessment, security reviews, rollout planning, and ongoing ownership models
Comfort working with technical teams and artifacts (PRDs/briefs, user stories, acceptance criteria, basic data models). Ability to prototype in low\-code/no\-code tools is a must
Strong analytical and measurement skills: can baseline, instrument, and report outcomes; can distinguish adoption activity from true impact
Excellent written and verbal communication: can create crisp briefs, run effective workshops, and communicate trade\-offs to both technical and non\-technical audiences
High ownership and bias to action; thrives in fast\-moving SaaS environments with evolving AI tooling
Bachelor’s degree in a related field (Business, Information Systems, Engineering, Computer Science) or equivalent practical experience
What’s in it for you?
Strong community: Work alongside top professionals in a friendly, open\-door environment
Growth focus: Take on large\-scale projects with a global impact and expand your expertise
Tailored learning: Boost your skills with internal events (meetups, conferences, workshops), Udemy access, language courses, and company\-paid certifications
Endless opportunities: Explore diverse domains through internal mobility, finding the best fit to gain hands\-on experience with cutting\-edge technologies
Care: Healthcare, Basic Life Insurance, Short and Long\-term disability insurance according to the Company’s Benefit Plans
About us:
At Ciklum, we are always exploring innovations, empowering each other to achieve more, and engineering solutions that matter. With us, you’ll work with cutting\-edge technologies, contribute to impactful projects, and be part of a One Team culture that values collaboration and progress. In the US, Ciklum is growing fast—inviting experienced professionals to lead digital transformation alongside Fortune 500 clients. Be part of a company where innovation and impact go hand in hand.
Explore, empower, engineer with Ciklum!
Interested already? We would love to get to know you! Submit your application. We can’t wait to see you at Ciklum.
\#LI\-MG1
Required Skills
AI Platform
Client Consulting
Intelligent Automation
Requirements definition and management
Job Info
Job Identification 4247
Job Category Intelligent Automation
Posting Date 08/12/2026, 04:43 AM
Locations US\-Remote (Remote)
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Ciklum, 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 in Demand for This Role
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 $214,900 based on 6,420 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 $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Ciklum AI Hiring
Ciklum has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.