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Ml Inference Jobs in Arizona (NOW HIRING)

AI Engineer

Phoenix, AZ · On-site

$100K - $120K/yr

... ML infrastructure • AI engineers with recent NodeJS/Javascript/Typescript experience • Proven ... inference tooling), with some exposure to TensorFlow • Strong schema, validation, and state ...

Sr. Analyst, Data Science

Tempe, AZ · On-site

$85.90K - $143.17K/yr

Stay current on advances in applied ML and bring emerging methods to bear on relevant business problems. Causal Inference & Experimentation * Design and analyze A/B tests and observational studies to ...

Sr. Devops & Cloud Engineer

Phoenix, AZ · On-site

$130K - $166.90K/yr

Build and manage custom Dataproc images and ML infrastructure on GCP. Deploy LLM inference workloads on Kubernetes (GKE) and ensure scalable infrastructure support. Monitoring, Observability ...

Sr. Analyst, Data Science

Tempe, AZ · On-site

$83.70K - $105.50K/yr

... causal inference--to answer business questions with rigor and clarity. • Translate complex ... ML and bring emerging methods to bear on relevant business problems. • Design and analyze A/B ...

... with ML researchers and engineers to seamlessly deploy new architectures into the production ... online inference. - Proficient in Python with a track record of writing high-quality, well ...

Senior Machine Learning Scientist

Scottsdale, AZ

$92.20K - $125.90K/yr

Design and implement efficient and scalable MLLM models for inference and analysis of multimodal ... PhD and with +5 years for ML Scientist, +8 years for Sr. ML Scientist, +10 years for Principal ML ...

AI and Data Science Engineer III

Tempe, AZ · On-site +1

$109.70K - $131.70K/yr

... science/ML, security, and platform engineering to deliver reliable, secure, and scalable AI ... inference, and LLM applications using Claude-, GPT/Codex-, and Gemini-class models, and more ...

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Ml Inference information

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 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 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 cities in Arizona are hiring for Ml Inference jobs? Cities in Arizona with the most Ml Inference job openings:
Infographic showing various Ml Inference job openings in Arizona as of May 2026, with employment types broken down into 22% Internship, and 78% Full Time. Highlights an 40% In-person, and 60% Remote job distribution.

$100K - $120K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted yesterday


Job description

Role - AI engineer
Experience Required - 8+ Years
Must Have Technical/Functional Skills
• 10+ years of experience building large-scale distributed systems + strong experience with LLM systems, agentic workflows or advanced ML infrastructure
• AI engineers with recent NodeJS/Javascript/Typescript experience
• Proven ownership of complex, cross-cutting agentic systems spanning multiple teams or products.
• Strong engineering fundamentals across backend systems, APIs, data pipelines, and cloud infrastructure.
• Deep experience across the agentic AI stack, including planning, tool use, memory, and evaluation.
• Fluency with AI-assisted and agentic development workflows.
• Comfort operating in ambiguous problem spaces and translating them into shipped, reliable autonomous systems.
• Ability to influence technical direction and align teams without formal authority.
• Experience in workflow engines, async processing, queues, and streaming systems.
• Languages: NodeJS/Javascript/Typescript,Python, Go
• APIs and services: REST, gRPC
• Cloud and infrastructure: AWS and/or GCP, Kubernetes
• Distributed systems: event-driven architectures, including Kafka
• Orchestration Frameworks: LangGraph, LangChain, AirFlow, etc
• Integration of commercial and open-source LLMs into agentic workflows
• Agent and orchestration frameworks such as LangChain, LlamaIndex, Semantic Kernel, or CrewAI, with strong judgment about when to use frameworks versus building lighter-weight primitives
• Model-level work using PyTorch and the Hugging Face ecosystem (embeddings, fine-tuning, inference tooling), with some exposure to TensorFlow
• Strong schema, validation, and state management practices using tools such as Pydantic (Python) and Zod (TypeScript)
• Experience building agentic systems in fintech or other regulated industries.
• Experience as a founding engineer or early technical leader in AI-driven products.
• Demonstrated success delivering technically complex autonomous systems that customers actively rely on.
• Meaningful contributions to open-source AI or agentic frameworks.
• Familiarity with fine-tuning, model optimization and inference pipelines is a plus.
Roles & Responsibilities
• Drive technical direction for agentic AI initiatives, influencing architecture patterns, autonomy boundaries, and system design.
• Design, build, and operate production-grade age ntic AI systems used across multiple products.
• Own and evolve shared agentic AI capabilities, including:
• Agent frameworks and orchestration layers
• Planning, tool use, and memory strategies
• Retrieval and grounding (RAG) pipelines
• LLM infrastructure, inference, and model gateways
• Evaluation, observability, and safety tooling for autonomous systems
• Lead technical design reviews and help teams navigate tradeoffs involving autonomy, safety, reliability, scalability, and cost.
• Partner across teams to deliver complex, cross-cutting agentic AI initiatives from concept to production.
• Evaluate emerging models, techniques, and agentic patterns and translate them into practical, enterprise-ready improvements.
• Mentor senior engineers and raise the technical bar for agentic AI development through example and influence.
Base Salary Range : $100,000 to $120,000 Per Annum
TCS Employee Benefits Summary:
Discretionary Annual Incentive.
Comprehensive Medical Coverage: Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans.
Family Support: Maternal & Parental Leaves.
Insurance Options: Auto & Home Insurance, Identity Theft Protection.
Convenience & Professional Growth: Commuter Benefits & Certification & Training Reimbursement.
Time Off: Vacation, Time Off, Sick Leave & Holidays.
Legal & Financial Assistance: Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing.