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Ml Inference Jobs in Ohio (NOW HIRING)

CSC Generation

Deep understanding of ML fundamentals beyond API-level usage, including model evaluation, validation, and failure mode diagnosis. * Strong grounding in causal inference and experimental design ...

Cerebras Systems

This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of ...

Deloitte

AI Engineer Consultant

Columbus, OH · Hybrid

Deliver governed data and features for ML/GenAI (curated datasets, feature pipelines/serving) supporting training and real-time inference, including consistency, caching, backfills, and latency SLOs.

Deloitte

AI Engineer Consultant

Cleveland, OH · Hybrid

Deliver governed data and features for ML/GenAI (curated datasets, feature pipelines/serving) supporting training and real-time inference, including consistency, caching, backfills, and latency SLOs.

Deloitte

AI Engineer Senior Consultant

Cincinnati, OH · Hybrid

$100K - $137K/yr

Deliver governed datasets and feature engineering/serving for ML training and real-time inference (online/offline consistency, caching, latency SLOs, backfills). * Implement safety, privacy, and ...

Deloitte

AI Engineer Senior Consultant

Columbus, OH · Hybrid

$100K - $138K/yr

Deliver governed datasets and feature engineering/serving for ML training and real-time inference (online/offline consistency, caching, latency SLOs, backfills). * Implement safety, privacy, and ...

Deloitte

AI Data Engineer - Senior Consultant

Cincinnati, OH · Hybrid

$100K - $137K/yr

Deliver governed datasets and feature engineering/serving for ML training and real-time inference (online/offline consistency, caching, latency SLOs, backfills). * Implement safety, privacy, and ...

Deloitte

AI Data Engineer - Senior Consultant

Columbus, OH · Hybrid

$100K - $138K/yr

Deliver governed datasets and feature engineering/serving for ML training and real-time inference (online/offline consistency, caching, latency SLOs, backfills). * Implement safety, privacy, and ...

Deloitte

AI Data Engineer - Senior Consultant

Dayton, OH · Hybrid

$101K - $139K/yr

Deliver governed datasets and feature engineering/serving for ML training and real-time inference (online/offline consistency, caching, latency SLOs, backfills). * Implement safety, privacy, and ...

Deloitte

AI Data Engineer - Senior Consultant

Cleveland, OH · Hybrid

$101K - $139K/yr

Deliver governed datasets and feature engineering/serving for ML training and real-time inference (online/offline consistency, caching, latency SLOs, backfills). * Implement safety, privacy, and ...

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Ml Inference information

What is ML inference?

ML inference refers to the process of using a trained machine learning model to make predictions or decisions based on new data. After a model has been trained on historical data, inference is the phase where that model is deployed and used in real-world applications, such as recognizing speech, detecting objects in images, or recommending products. The focus in ML inference is on speed, efficiency, and scalability to ensure quick predictions, often in real time. This process is critical for practical applications like mobile apps, web services, and embedded systems. Optimizing inference involves reducing latency, memory usage, and computational requirements.

What is the difference between Ml Inference vs Data Scientist?

AspectML InferenceData Scientist
Required CredentialsKnowledge of machine learning models, programming skillsDegree in data science, statistics, or related fields
Work EnvironmentDeploying models in production, real-time data processingData analysis, model development, research
Industry UsageAI product deployment, software companiesResearch institutions, tech firms, consulting

ML Inference focuses on deploying trained models to make predictions on new data, often in real-time. Data Scientists develop and analyze models, working primarily in research and development. While both roles require understanding of machine learning, ML Inference emphasizes deployment and operationalization, whereas Data Scientists focus on model creation and analysis.

Which 3 jobs will survive AI?

For ML Inference roles, jobs that require complex problem-solving, creativity, and emotional intelligence are more likely to persist, such as data scientists, AI ethics specialists, and machine learning engineers. These roles involve tasks that are difficult to automate and often require specialized skills, domain knowledge, and critical thinking. Continuous learning and expertise in AI tools and programming languages like Python or TensorFlow can also enhance job security in this field.

What engineers make $500,000?

Senior machine learning engineers with extensive experience, specialized skills in deep learning, and strong industry demand can earn $500,000 or more annually, especially in high-cost-of-living areas or within top tech companies. Achieving this level typically requires advanced degrees, certifications, and a proven track record of impactful projects.

What is a $900,000 AI job?

A $900,000 AI job typically refers to high-level roles in artificial intelligence, such as senior machine learning engineers or AI research directors, often requiring advanced skills in deep learning, data science, and experience with tools like TensorFlow or PyTorch. These positions usually involve leadership responsibilities, strategic planning, and may require multiple years of specialized experience or advanced degrees.

Is ML a high paying job?

Machine Learning (ML) inference roles are generally well-paid due to the specialized skills required, such as knowledge of algorithms, programming, and data analysis. Salaries vary based on experience, location, and industry, but they tend to be higher than average for tech positions. Advanced roles often require proficiency with tools like TensorFlow or PyTorch and may include certifications or advanced degrees.

What are some common challenges faced by ML Inference Engineers when deploying models to production?

ML Inference Engineers often encounter challenges such as optimizing model latency and throughput to meet production requirements, ensuring compatibility with diverse hardware environments, and managing model versioning and updates without disrupting service. Additionally, balancing resource utilization and inference accuracy while monitoring real-time performance metrics is crucial. Collaboration with data scientists, DevOps, and software engineers is typically essential to streamline deployment and maintain robust, scalable inference pipelines.

What are the key skills and qualifications needed to thrive in ML Inference, and why are they important?

To thrive in ML Inference, you need a solid background in machine learning principles, programming (Python or C++), and experience with deploying models at scale, often supported by a degree in computer science or a related field. Familiarity with frameworks and tools such as TensorFlow, PyTorch, ONNX, and cloud platforms like AWS SageMaker or Google AI Platform is typically required. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for collaborating with multidisciplinary teams and optimizing model performance. These skills ensure efficient, scalable, and reliable deployment of machine learning solutions in real-world applications.
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What job categories do people searching Ml Inference jobs in Ohio look for? The top searched job categories for Ml Inference jobs in Ohio are:
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Ml Inference jobs near you
Distinguished AI/ML Engineering Lead

Distinguished AI/ML Engineering Lead

Frontier Technology Inc.

Dayton, OH • On-site, Remote

$99K - $131K/yr

Full-time

Posted 16 hours ago

Job description

Overview
FTI Defense delivers mission-focused solutions to the Department of Defense and Intelligence Community through advanced engineering, digital transformation, and program execution expertise. We help our customers solve complex challenges and achieve mission success by integrating people, process, and technology.
FTI Defense is seeking a Distinguished AI/ML Engineer to serve as a technical leader, architect, and integrator - designing, building, deploying, and sustaining AI systems that transform complex mission data into trusted, explainable insights.
This is a hands-on builder role, not an analytics management position. The ideal candidate is equally comfortable writing model code, standing up ML pipelines, and integrating AI inference services into operational systems within secure environments. The right candidate blends deep AI/ML engineering expertise with system-level architecture leadership and an ability to unify data engineering, simulation modeling, and responsible AI principles into scalable, mission-ready capabilities.
Responsibilities
  • Architect and integrate hybrid AI systems that combine traditional machine learning, deep learning, large language models (LLMs), and retrieval-augmented generation (RAG) pipelines.
  • Design and deploy scalable AI architectures including APIs, microservices, and model-serving frameworks that integrate seamlessly with analytic, simulation, or operational systems.
  • Lead the full AI/ML lifecycle - from data ingestion and feature engineering through training, deployment, and sustainment within secure DoD environments (IL5/IL6, ATO, GovCloud).
  • Engineer event-driven data pipelines and feature stores for both structured and unstructured data, including text, imagery, and simulation outputs.
  • Ensure Responsible AI practices by embedding traceability, explainability, and confidence scoring into deployed systems.
  • Implement and maintain MLOps pipelines (MLflow, Kubeflow, Airflow, Docker/Kubernetes) to support continuous integration, retraining, and drift detection.
  • Transition R&D prototypes into production, optimizing for mission constraints such as limited compute, edge environments, or disconnected operations.
  • Provide technical leadership and mentorship, setting standards for model quality, architectural design, and ethical AI deployment across programs.
  • Collaborate across engineering, data, and modeling teams to unify FTI's AI portfolio, ensuring interoperability and reuse across mission systems.
  • Support proposal and solution development, providing technical inputs for AI/ML architectures, data strategies, and Responsible AI assurance frameworks.

Education/Qualifications
  • Active Secret clearance required; TS/SCI strongly preferred.
  • Bachelor's degree in Computer Science, Engineering, or a related technical field (Master's or Ph.D. preferred).
  • 10+ years of overall experience in AI/ML development, with 5+ years designing and deploying scalable AI/ML architectures, including at least two full lifecycle implementations (from prototype to operational system).
  • Proficiency in Python, PyTorch, TensorFlow, and modern ML frameworks.
  • Experience designing or deploying systems using vector databases (Milvus, Pinecone, Weaviate), knowledge graphs, and semantic search frameworks.
  • Proven ability to design event-driven data pipelines using Databricks, Spark, Flink, or Kafka.
  • Demonstrated experience deploying AI/ML systems in secure, classified, or edge environments.
  • Familiarity with Responsible AI and assurance principles, including bias detection, explainability, human-machine teaming, and hallucination prevention.
  • Experience integrating AI models into simulation, modeling, or operational planning systems is highly desirable.
  • Experience transitioning R&D systems into accredited production environments.
  • Strong communication and mentoring skills, with the ability to lead technically while remaining deeply hands-on.

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