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About This Role
Title: AI/ML Engineer Number of Openings: 2 Location: Malvern, PA (3 days on\-site required)
Interview Process
Systems Technical Screening (1 hour MS Teams Video) 1 hour MS Teams Video I/V with client team
Core Responsibilities
- Agentic AI \& MCP Integration: Implement agentic frameworks (e.g., LangGraph, AutoGen) and Model Context Protocol (MCP) for secure tool orchestration.
- Generative AI Development: Build LLM\-based applications with RAG, structured output, and evaluation frameworks.
- Agentic Cloud Deployment \& Integration: Design and deploy agentic AI services in cloud environments, integrating models, tools, APIs, and data sources to deliver scalable, autonomous workflows.
- Databricks \& Lakehouse Engineering: Develop and optimize ML and GenAI workloads using Databricks, including Sparkbased data pipelines, feature engineering, and model training/inference on the Lakehouse platform.
- Unity Catalog \& Governance: Implement Unity Catalog for centralized data, model, and feature governance, ensuring secure access control, lineage tracking, and compliance across ML and GenAI assets.
- AWS ML Engineering: Deploy models using SageMaker pipelines, ECS/ECR, Lambda; manage CI/CD and monitoring.
- Security \& Identity: Integrate Okta/JWT token for API and service authentication; enforce token validation and claims.
- Governance : Deliver artifacts required by MDLC/MPLC (Model Documents, Data Dictionary, Monitoring Plan).
- Collaboration: Partner with PO, and business stakeholders to align solutions with objectives.
Responsibilities
- Design, develop, and optimize complex data pipelines using machine learning engineering best practices to ensure scalability, efficiency, and reliability.
- Develop and implement robust MLOps pipeline to support the deployment, monitoring, and lifecycle management of AI/ML models in production environments.
- Integrate and maintain data and model pipelines, proactively diagnosing data quality issues and documenting assumptions.
- Collaborate closely with data scientists to validate model\-ready datasets and ensure thorough, accurate feature documentation.
- Conduct exploratory data analysis and discovery on raw data sources, incorporating business context to support model development.
- Track data lineage and perform root cause analysis during early\-stage exploration or issue resolution.
- Partner with internal stakeholders to understand business processes and translate them into scalable analytical solutions.
- Develop and maintain model monitoring scripts, investigate alerts, and coordinate timely resolutions.
- Act as a subject matter expert in machine learning engineering on cross\-functional teams, contributing to high\-impact initiatives.
- Stay current with advancements in AI/ML and evaluate their applicability to business challenges.
Qualifications
- Bachelor s degree in Computer Science, Engineering, or related field (Master s preferred).
- 6\+ years of experience across Artificial Intelligence (AI) / Machine Learning (ML) engineering, data engineering, and MLOps implementation, including: Designing and deploying production\-grade ML systems.
- Building scalable data pipelines and ML workflows.
- Managing model lifecycle in cloud environments.
- Proficient in Python and familiar with ML frameworks such as TensorFlow, PyTorch, and Scikit\-learn.
- Hands\-on experience with Databricks, including: Sparkbased data processing and feature engineering Databricks ML/MLflow for experiment tracking and model management Integrating Databricks with cloudnative ML services
- Experience implementing Unity Catalog for centralized governance of data, features, and models, including access controls, lineage, and auditability.
- Strong understanding and experience in AWS Machine Learning Stack including: AWS SageMaker AWS Glue AWS Bedrock AWS Data Pipelines AWS Lambda Functions
- Experience with Generative AI model development building LLM based applications with RAG.
- Experience implementing agentic frameworks (e.g., LangGraph, AutoGen) and Model Context Protocol (MCP) for orchestration.
- Knowledge of React UI, GraphDB, and GenAI model performance evaluation
- Experience with CI/CD, containerization (e.g., Docker), and orchestration tools (e.g., Kubernetes).
- Solid grasp of software engineering principles including testing, version control (e.g., Git), and security.
- Familiarity with the Machine Learning Development Lifecycle (MDLC) and best practices for reproducibility and scalability.
- Strong communication and collaboration skills, with experience working across technical and business teams.
- Ability to anticipate ambiguity and devise scalable solutions to address it.
Nice to Have
- Knowledge of data governance, model explainability, and responsible AI practices.
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. Mid-level AI roles across all categories have a median of $131,300.
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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