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Ml Inference Jobs in Louisiana (NOW HIRING)

Deliver governed datasets and feature engineering/serving for ML training and real-time inference (online/offline consistency, caching, latency SLOs, backfills). A successful candidate would possess ...

Running AI/ML workloads for training and inference on AWS, Azure, or GCP (e.g., SageMaker, Azure ML, Vertex AI) . Processing and managing large scale (TB scale) datasets and their associated data ...

Google AI Lead Architect

New Orleans, LA · On-site

$53 - $72.75/hr

Integrate and fine-tune Large Language Models (LLMs) and other AI/ML models into enterprise applications. Develop and implement strategies for model deployment, inference, and monitoring, with an ...

Integrate and fine-tune Large Language Models (LLMs) and other AI/ML models into enterprise applications. Develop and implement strategies for model deployment, inference, and monitoring, with an ...

Serve as a subject matter expert in ML, advanced AI, GenAI/LLMs, Snowflake Cortex, Epic deployment, applied statistics, causal inference, and experiment design. Provide technical mentorship through ...

Serve as a subject matter expert in ML, advanced AI, GenAI/LLMs, Snowflake Cortex, Epic deployment, applied statistics, causal inference, and experiment design. Provide technical mentorship through ...

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.

What are popular job titles related to Ml Inference jobs in Louisiana?

For Ml Inference jobs in Louisiana, the most frequently searched job titles are:

What cities in Louisiana are hiring for Ml Inference jobs?

Cities in Louisiana with the most Ml Inference job openings:

Senior Technical Marketing Engineer

Central, LA • On-site

Lightbits Labs
Software Development • 51 - 200 employees

$110 - $160/hr

Other

Posted 6 days ago


Job description

We are looking for a hands-on technical marketing professional who can translate deep infrastructure knowledge into commercial outcomes. In this role you will report to the SVP of AI Product & Business, spanning hands-on technical enablement, partnership development, and go-to-market execution of our flagship KV cache acceleration product, Inferra. You will partner closely with the engineering team (architecture, deployment, solutions) to turn technical capability into content, guides, and technical partnerships that drive adoption. You will own the technical story of Inferra from product positioning and market education through POVs, proof points, sales/channel enablement and field execution. You will work directly with prospective customers to help them successfully evaluate and implement Inferra, capture the technical and business outcomes of those deployments, and turn those learnings into compelling customer-facing content and sales assets

This position is located in Central/ Midwest, USA.

Responsibilities
  • Deployment & technical enablement: Build and maintain deployment guides, reference architectures, and technical documentation for NeoCloud and Inference customers evaluating or deploying our products
  • Hands-on validation: Deploy and debug Kubernetes clusters and inference stacks yourself to validate claims, reproduce customer issues, and keep collateral technically authentic.
  • Partnerships: Identify, develop, and manage strong technical relationships with NeoClouds, GPU cloud providers, and ecosystem players (inference frameworks, hardware vendors) to expand product footprint
  • Technical marketing content: Translate performance data (KV cache hit rates, TTFT, session density, cost-per-token) into customer-facing narratives, briefing decks, and competitive positioning
  • Sales enablement: Create battle cards, ROI/pricing tools, and technical FAQs that help the sales team and partners sell the product credibility
  • GTM execution: Conference demos, technical collaterals, blogs and website content.
Qualifications
  • Prior Experience in the technology domain: 5+ years
  • Bachelors in Computer Science or Electronics/Electrical engineering, MBA is a plus
  • Experience in the AI infrastructure space, preferably at a NeoCloud, GPU cloud provider, or inference software company
  • Hands-on experience deploying and operating Kubernetes clusters, including debugging real production issues
  • Working knowledge of the inference stack—including KV cache, GPU memory constraints, and throughput/latency tradeoffs— enough to have a detailed technical conversation with an ML infra engineer
  • Track record of applying infrastructure efficiency improvements through software stack optimization.
  • Familiarity with inference serving frameworks like vLLM, SGLang, or others
  • Strong business acumen: comfortable with partnership negotiation and commercializing technical products
  • Excellent verbal and written communication — able to produce clear, technically accurate customer-facing content
  • Startup mentality: High ownership, comfortable with ambiguity, and willing to do work above and below your core domains
  • Prior technical marketing, application or solutions engineering experience in AI/ML infrastructure or AI model efficiency teams will be a plus
  • Existing relationships in the NeoCloud/Inference-as-a-Service ecosystem and GPU cost/performance modeling is a plus
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