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Ml Inference Jobs in Callahan, FL (NOW HIRING)

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Ml Inference information

See Callahan, FL salary details

$33.3K

$108.9K

$174.3K

How much do ml inference jobs pay per year?

As of Jul 26, 2026, the average yearly pay for ml inference in Callahan, FL is $108,881.00, according to ZipRecruiter salary data. Most workers in this role earn between $87,400.00 and $120,600.00 per year, depending on experience, location, and employer.

What is a $900000 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 involving advanced skills in deep learning, data modeling, and programming with tools like Python and TensorFlow. These positions usually require extensive experience, specialized knowledge, and may include leadership responsibilities or strategic decision-making.

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.

What engineer makes $500,000 a year?

Senior machine learning engineers with extensive experience, advanced skills in deep learning, and expertise in deploying large-scale models can earn salaries approaching or exceeding $500,000 annually, especially in high-cost-of-living areas or top tech companies. Compensation often includes base salary, bonuses, and stock options, reflecting their specialized knowledge and impact on product development.

Which 3 jobs will survive AI?

Jobs involving Ml Inference, such as data scientists, machine learning engineers, and AI system architects, are likely to persist as they require specialized expertise in developing, deploying, and maintaining AI models. These roles demand critical thinking, domain knowledge, and skills in programming and data analysis that are less easily automated. Continuous learning and staying updated with AI tools and frameworks are essential for these professions to remain relevant.

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.

Will MLE be replaced by AI?

Machine Learning Engineers (MLEs) design, develop, and optimize AI models and systems. While AI automation tools can assist with certain tasks, MLEs are essential for building, tuning, and maintaining complex models, making complete replacement unlikely in the near term. Their expertise in data handling, model deployment, and system integration remains critical in AI development environments.

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.
What cities near Callahan, FL are hiring for Ml Inference jobs? Cities near Callahan, FL with the most Ml Inference job openings:
Ml Inference jobs near you
Infographic showing various Ml Inference job openings in Callahan, FL as of July 2026, with employment types broken down into 78% Full Time, 20% Part Time, and 2% Contract. Highlights an 75% Physical, 2% Hybrid, and 23% Remote job distribution, with an average salary of $108,881 per year, or $52.3 per hour.
Information Technology_USA - USA_Engineer

Information Technology_USA - USA_Engineer

Real Soft, Inc.

Jacksonville, FL • On-site

$106K - $127K/yr

Contractor

Posted 23 days ago

Job description

: MAX CONFIRMED
Location: ONSITE- Woodland Hills, CA
Duration: 6 months
COMPLETE ROLE CHANGE PLEASE PIVOT AND HELP WITH NEW PROFILES WHILE WE GET A NEW REQ PUSHED**
Role: Senior AIOps ML Engineer
Descriptions:
"Core Responsibilities
Lakehouse Architecture & Data Engineering
• Schema Design: Design and evolve the Lakehouse schema (Delta Lake / Apache Iceberg) for multi-domain observability data at petabyte scale.
• Pipeline Engineering: Build and maintain robust ingestion pipelines from the OTel Collector through Kafka to the Lakehouse, ensuring exactly-once semantics and strict schema enforcement.
• Data Transformation: Implement dbt transformation models to generate mart-ready, denormalized fact and dimension tables for each of the six domains.
• Data Quality Governance: Define and enforce data quality contracts, establishing SLAs for data freshness, completeness, and cardinality budgets per mart.
• Performance Optimization: Optimize query performance utilizing partitioning strategies, Z-ordering, bloom filters, and materialized views tailored for time-series patterns.
ML Model Development & AIOps
• AIOps Modeling: Design, train, and deploy machine learning models for streaming multivariate anomaly detection, root-cause analysis, and incident forecasting across all six mart domains.
• Streaming Inference: Build low-latency streaming inference pipelines (Flink / Spark Streaming) for real-time anomaly scoring on APM, infrastructure, and security signals.
• Log Intelligence: Develop sophisticated log intelligence models-including clustering (DRAIN3 / LogBERT), NLP classification, and error deduplication-over the Log mart.
• Behavioral Analytics: Implement unsupervised and semi-supervised methods for User Experience frustration detection and KPI correlation analysis.
• Feature Store Management: Own the ML feature store, managing feature engineering, versioning, backfill pipelines, and point-in-time correct joins for training datasets.
• Model Lifecycle MLOps: Instrument model performance tracking, including drift detection, accuracy monitoring, and automated retraining triggers.
AIOps Platform & Productionization
• Workflow Orchestration: Design and operate the end-to-end AIOps workflow, spanning signal ingestion, feature computation, model inference, alert routing, and auto-remediation hooks.
• Model Serving Infrastructure: Build high-performance model serving infrastructure-supporting real-time REST/gRPC endpoints and async batch scoring-with strict p99 latency SLOs.
• Incident Tool Integration: Integrate AIOps insights with incident management platforms (PagerDuty, Opsgenie) and internal runbooks to deliver enriched, noise-reduced alerting.
• Business Impact Quantification: Define and publish metrics from the Business KPI mart to quantify the blast radius, revenue loss, and affected user counts for each incident.
Security & Compliance Observability
• Security Mart Collaboration: Partner with the Security team to build the Security mart schema, including threat feed ingestion, UEBA baselines, and CVE correlation pipelines.
• Threat Detection: Train anomalous-access and lateral-movement detection models, tuning precision/recall thresholds in collaboration with the SOC team.
• Compliance & Governance: Ensure all data handling across the marts adheres strictly to data residency requirements, PII masking standards, and audit-log protocols.
Collaboration & Engineering Standards
• Schema Contracts: Define telemetry schema contracts with the OTel Instrumentation team to guarantee high upstream signal quality for downstream ML models.
• Organizational Standards: Author ML platform RFCs and contribute actively to observability data model standards across the broader engineering organization.
• Mentorship & Reviews: Mentor junior ML and data engineers, and conduct rigorous design reviews for new mart schemas and model architectures."
✅ Kafka + Streaming (Flink/Spark)
✅ Lakehouse (Delta / Iceberg)
✅ ML (Anomaly detection + time-series)
✅ Observability (OTel, APM, Logs)
✅ MLOps (feature store, drift, retraining)
✅ SQL + Python (strong)
Skills: AI Agents
Experience Required: 10 & Above, Project Code :

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