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Ml Inference Jobs in Florida (NOW HIRING)

Mosai

Machine Learning Operations Engineer

Jacksonville, FL · On-site +1

$47.50 - $65/hr

Design, build, and maintain scalable data pipelines supporting model training, inference, batch processing, and real-time analytics workflows. * Audit, refactor, and consolidate existing ML pipelines ...

Virtusa

Agentic AI developer

Tampa, FL · On-site

$114K - $154K/yr

Experience designing and deploying machine learning systems across training, inference, and ... ML systems * Strong expertise in Python and data libraries (NumPy, Pandas, etc.) * Proven ...

AAA Global

Machine Learning Researcher

Miami, FL · On-site

Produce trading and predictive signals using innovative ML algorithms * Apply the latest LLM-based ... Advanced practitioner-level knowledge of statistical inference, machine learning, software solvers ...

YO AI Labs

Mental Health Expert - Remote

Miami, FL · Remote

$200 - $350/hr

AI/ML Engineer Job Type: Full-Time Location: Remote Job Summary We are seeking an experienced AI/ML ... for AI training and inference. * Develop and maintain REST APIs and SDK integrations

247Hire

Machine Learning Engineer

Orlando, FL · On-site

Production AI/ML Systems: Design A/B testing frameworks for generative model comparison and optimization * Build real‑time inference optimization for low‑latency content generation * Implement ...

Total Quality Logistics

AI Architect

Tampa, FL · On-site

Proven experience architecting and delivering production AI or ML solutions on Azure * Experience ... Lead solution designs for AI platforms including vector databases, embedding pipelines, inference ...

TQL

AI Architect

Tampa, FL · On-site

$59.50 - $78.50/hr

Proven experience architecting and delivering production AI or ML solutions on Azure* Experience ... Lead solution designs for AI platforms including vector databases, embedding pipelines, inference ...

Cisco

Applied AI Scientist

Miami, FL

Explore and evaluate new AI/ML techniques, tools, and methodologies, applying relevant innovations ... and inference efficiency to minimize cost and latency while preserving accuracy. * MLOps ...

Cisco

Applied AI Scientist

Tampa, FL

Explore and evaluate new AI/ML techniques, tools, and methodologies, applying relevant innovations ... and inference efficiency to minimize cost and latency while preserving accuracy. * MLOps ...

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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 are the key skills and qualifications needed to thrive in ML inference?

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 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 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.

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The top searched job categories for Ml Inference jobs in Florida are:

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Ml Inference jobs near you

Senior Principal Data Scientist / AI-ML SME (Analytic Superiority) (Fort Meade)

Fort Meade, FL • On-site

Via Logic LLC
See more jobs at Via Logic LLCexternal

$154K - $278K/yr

Full-time

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Mission Overview

The Leidos Intel Sector is looking for a premier AI/ML Subject Matter Expert (SME) to serve as a Technical Closer for our COSS 3.0 program supporting USCYBERCOM and the Cyber National Mission Force (CNMF) at Fort Meade, MD. In this elite role, you will architect and engineer the cross‑platform AI frameworks required to achieve absolute analytic superiority.

You will compress both defensive cyber operations (identifying network vulnerabilities and gaps) and offensive operations (vulnerability discovery and automated targeting) from days down to minutes. As a Technical Closer, you will mentor senior technologists by example-working fingers-on-keyboard to solve the command's most complex technical roadblocks and push production‑grade AI directly into multi‑cloud, hybrid, and air‑gapped mission enclaves.

Core Technical Requirements
  • Platform‑Agnostic Infrastructure & MLOps: Architect, deploy, and scale distributed AI workloads across any environment required, including AWS SageMaker, Google Vertex AI, Azure Government, and bare‑metal, air‑gapped server racks.
  • Agentic AI & Cyber Automation: Deploy and optimize tools like LangGraph, CrewAI, or AutoGPT to automate cyber threat identification and offensive target generation at wire speed.
  • Low‑Level Model Engineering & Optimization: Fine‑tune open‑source large language models (e.g., Llama 3, Mistral) inside secure enclaves using PyTorch or TensorFlow. Utilize NVIDIA TensorRT, Triton Inference Server, vLLM, and quantization libraries (bitsandbytes) to compress models for high‑throughput execution under strict hardware constraints.
  • Advanced RAG Architectures: Direct the engineering of enterprise Retrieval‑Augmented Generation (RAG) stacks using LangChain paired with high‑performance vector databases like Milvus, Qdrant, or Pinecone.
  • Autonomous Cyber Integration: Connect intelligent agents directly into security orchestration platforms (e.g., Palo Alto Cortex XSIAM/XSOAR) to trigger automated network defense actions and ingest massive, real‑time PCAP and telemetry streams via Apache Kafka/Spark.
  • Polyglot Engineering: Demonstrate engineering mastery in Python, Go, Rust, and C/C++ to build ultra‑fast cyber tools, write optimized GPU kernels, and interface with distributed frameworks like Ray.
Mission & Domain Expertise
  • Dual‑Spectrum Operations: Proven capability to support both Defensive Cyber Operations (DCO) (log parsing, behavioral threat hunting, anomaly detection) and Offensive Cyber Operations (OCO) (automated vulnerability discovery, exploit generation, payload optimization).
  • Mission Platform Orchestration: Experience integrating custom AI/ML pipelines into unified mission systems and high‑value data streams found across Project Maven, Palantir Foundry, and tactical command frameworks.
Required Experience & Background
  • Total Technical Experience: 15+ years of hands‑on experience in software engineering, data science, or distributed systems.
  • Core AI/ML Focus: 5+ years of specialized experience in Machine Learning Engineering, deep learning, or LLM optimization.
  • DoD/IC Ecosystem: 3-5 years working within the DoD/IC cyber ecosystem, specifically building tools that map vulnerabilities or accelerate targeting cycles.
  • Clearance: Active TS/SCI with Polygraph.
  • Work Location: On‑site at Fort Meade, MD (SCIF environment).
Preferred Certifications & Military Equivalency
  • Industry Certifications: Google Cloud Professional Machine Learning Engineer, AWS Certified Machine Learning - Specialty, or NVIDIA Generative AI/LLM Associate.
  • Cyber Mission Force (CMF) Equivalency: Prior certification as a CMF Exploitation Analyst (EA), Digital Network Analyst (DNA), or specialized technical experience as an Army 17A/170A, Navy 181X, or Air Force 17D/17S.
Educational Background
  • Primary Requirement: Master's Degree or PhD in Data Science, Artificial Intelligence, Computer Science, Mathematics, or a related quantitative field. Additional years of experience may be considered in lieu of a degree.
Pay Range

Pay Range $154,050.00 - $278,475.00

Commitment to Non-Discrimination

All qualified applicants will receive consideration for employment without regard to sex, race, ethnicity, age, national origin, citizenship, religion, physical or mental disability, medical condition, genetic information, pregnancy, family structure, marital status, ancestry, domestic partner status, sexual orientation, gender identity or expression, veteran or military status, or any other basis prohibited by law. Leidos will also consider for employment qualified applicants with criminal histories consistent with relevant laws.

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