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About This Role
Role Summary: This role is focused on building agentic AI systems and establishing a core AI capability within the organization. The engineer will design, develop, and deploy advanced AI agents and create foundational frameworks for future AI\-driven solutions.
Key Responsibilities:
- Core Development \& Architecture: Analyze, design, develop, test, and implement complex applications. Build enterprise\-level applications and custom integrations. Design, code, debug, and document software solutions. Evaluate system interdependencies and impacts of changes.
- AI \& Advanced Systems: Develop and deploy agentic AI systems. Build frameworks for generative AI use cases. Research, test, and optimize AI models and agent frameworks. Monitor and improve AI behavior in production environments.
- Technical Leadership (20%): Lead integration of applications across business systems. Design scalable structures and solutions for enterprise software.
- Consulting \& Collaboration (15%): Act as internal consultant and mentor. Work with stakeholders to define business and technical requirements. Ensure alignment with IT strategy and architecture standards.
- Strategic Design \& Innovation (15%): Recommend long\-term IT and architecture improvements. Evaluate build vs. buy decisions. Contribute to data and component architecture design.
- System Development \& Problem Solving (15%): Solve complex business and technical problems. Develop new approaches, techniques, and data sources.
- Standards \& Lifecycle Management (15%): Define development standards and best practices. Participate in full SDLC (design deployment support). Ensure timely and budget\-conscious delivery.
- Leadership \& Mentorship (15%): Guide junior developers and analysts. Lead or coordinate complex projects. Resolve cross\-team technical issues.
- Testing \& Documentation (5%): Perform testing, debugging, and validation. Maintain technical documentation.
Required Skills \& Qualifications:
- Bachelor's degree in Computer Science, IT, or related field OR 4 years relevant experience OR Associate's degree \+ 2 years relevant experience.
- 8\+ years in application development, systems testing, or related fields. 3 6 years in AI/ML or related domains.
- Technical Skills:
+ Core Technologies: Python (advanced proficiency), JavaScript / TypeScript, API design, AI / ML \& Agentic Systems, Generative AI development (end\-to\-end), Agentic AI concepts (reasoning, planning, tool use), Prompt engineering, RAG, and AI system design.
+ Experience with AI models (e.g., OpenAI, Claude).
+ Frameworks \& Tools: LangChain, LangGraph, and similar frameworks. AI agent development tools and ecosystems.
+ Infrastructure \& DevOps: AWS (preferred) or other cloud platforms, CI/CD pipelines, Docker \& Kubernetes, GitHub and modern development workflows.
+ Systems Knowledge: Multi\-platform environments (mainframe, midrange, PC/LAN), Software architecture and system integration, Performance monitoring and optimization.
+ Nice\-to\-Have Skills: FastAPI or Flask, SQL and database experience, AutoGen, MCP, embeddings, knowledge stores, Reinforcement learning and planning algorithms, Multi\-agent systems, Multi\-cloud experience (AWS, Azure, Google Cloud Platform), Model fine\-tuning and advanced prompt engineering.
- Soft Skills: Strong analytical and problem\-solving abilities. Effective verbal and written communication. Ability to work under pressure in fast\-paced environments. Team collaboration and leadership skills. Attention to detail. Strong interpersonal and relationship\-building skills.
Work Environment: Fast\-paced, project\-oriented environment. Multi\-platform technical ecosystem. May require occasional 24/7 responsiveness. Strong focus on innovation, collaboration, and customer needs.
Day\-to\-Day Activities: Hands\-on Python development for AI systems. Designing and implementing AI architectures. Integrating AI agents with backend services. Deploying solutions via CI/CD pipelines. Researching and testing new AI models/frameworks. Collaborating with data scientists and stakeholders. Monitoring and improving production AI systems.
Team \& Culture: Innovative, collaborative, and fast\-paced. Focused on building next\-generation enterprise AI solutions. Emphasis on creativity, continuous learning, and impact. Opportunity to shape long\-term AI strategy.
Requires strong hands\-on Python skills and a deep understanding of end\-to\-end RAG pipeline design. Must explain the full RAG implementation and Python programming proficiency.
For applications and inquiries, contact: hirings@openkyber.com
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Openkyber, 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 $166,983 based on 13,781 positions with disclosed compensation. Entry-level AI roles across all categories have a median of $76,880.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Openkyber AI Hiring
Openkyber has 161 open AI roles right now. They're hiring across AI/ML Engineer, AI Consultant, AI Engineering Manager, MLOps Engineer. Positions span GA, US, NJ, US, IL, US. Compensation range: $120K - $199K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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