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Deep Learning Accelerator Jobs in Ohio (NOW HIRING)

Infosys Limited

AI Architect

Blue Ash, OH · On-site

... accelerators. * Develop insights, whitepapers, and proof-of-concept summaries that highlight innovative thinking. * Review innovative models and applications in non-ML, ML, deep learning, or LLM ...

Infosys

AI Architect

Cincinnati, OH · On-site

$61.25 - $78.75/hr

... accelerators. • Develop insights, whitepapers, and proof-of-concept summaries that highlight innovative thinking. • Review innovative models and applications in non-ML, ML, deep learning, or LLM ...

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Deep Learning Accelerator information

What is a deep learning accelerator?

Deep Learning Accelerators are specialized hardware or systems designed to speed up the processing and training of deep learning algorithms, such as neural networks. They are optimized for the heavy computational demands of tasks like image recognition, natural language processing, and other AI applications. Examples include Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), and custom-designed chips like Application-Specific Integrated Circuits (ASICs) and Field-Programmable Gate Arrays (FPGAs). These accelerators enable faster data processing, lower power consumption, and improved efficiency compared to general-purpose CPUs. As AI applications grow, the use of deep learning accelerators is becoming increasingly important in both research and industry.

What skills and qualifications are needed to thrive as a deep learning accelerator engineer?

To thrive as a Deep Learning Accelerator Engineer, you need a strong background in computer engineering, digital design, and machine learning, typically supported by a degree in computer science or electrical engineering. Experience with hardware description languages (such as Verilog or VHDL), FPGA/ASIC toolchains, and familiarity with deep learning frameworks like TensorFlow or PyTorch is essential. Problem-solving, teamwork, and effective communication are crucial soft skills for collaborating with cross-functional teams and translating algorithmic requirements into efficient hardware solutions. These skills are vital to designing high-performance, energy-efficient hardware accelerators that advance AI capabilities and meet industry demands.

What are the main challenges faced when optimizing deep learning models for hardware accelerators?

One of the primary challenges in this role is bridging the gap between deep learning model requirements and the constraints of specialized hardware, such as GPUs, TPUs, or custom ASICs. This often involves model quantization, memory optimization, and adapting algorithms to exploit hardware parallelism while maintaining accuracy and efficiency. Collaboration with both hardware engineers and software developers is essential to ensure models run efficiently on target platforms, and staying current with evolving accelerator architectures is key to long-term success.

What is the difference between Deep Learning Accelerator vs Machine Learning Engineer?

AspectDeep Learning AcceleratorMachine Learning Engineer
Required CredentialsKnowledge of hardware design, FPGA/ASIC programming, deep learning frameworksDegree in Computer Science, Data Science, or related fields; experience with ML frameworks
Work EnvironmentHardware development labs, embedded systems, AI hardware companiesSoftware development environments, tech companies, research labs
Industry UsageAI hardware manufacturing, embedded AI solutionsAI/ML software development, data analysis, model deployment
Search & Comparison IntentFocus on hardware acceleration, AI hardware designFocus on software development, model building

Deep Learning Accelerators specialize in hardware design and optimization for AI workloads, working closely with hardware and embedded systems. Machine Learning Engineers develop and deploy ML models primarily through software, focusing on algorithms and data. While both roles involve AI, their core skills, work environments, and industry applications differ significantly.

Deep Learning Accelerator jobs near you
Infographic showing various Deep Learning Accelerator job openings in Ohio as of June 2026, with employment types broken down into 53% Full Time, and 47% Part Time. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

AI Architect

Blue Ash, OH • On-site

Infosys Limited
Business Management Consulting • 10K+ employees
Learn more about Infosys Limitedexternal

Other

Medical, Retirement

Posted 9 days ago

Infosys rating

7.0

Company rating: 7.0 out of 10

Based on 62 frontline employees who took The Breakroom Quiz

Job description

State / Region / Province

Arizona, Connecticut, Indiana, North Carolina, Ohio, Texas

Country

USA

Domain

Delivery

Interest Group

Company

ITL USA

Requisition ID

148821BR

Technical Skills 1

Technical Skills 2

Overview

The Infosys Retail, Consumer Goods, and Logistics unit stands as a globally respected partner of choice, dedicated to helping clients achieve their business goals through cutting-edge technology and seamless services. Our unit offers a dynamic forum where projects and teams can effectively learn, adopt, and excel in all technologies. We foster a vibrant community that leverages shared skills and experiences to deliver high-quality, value-enhanced solutions. Join us and become part of a team that drives innovation, operational efficiency, and sustainable growth in the retail, consumer goods, and logistics sector. Together, we can shape the future of these industries and achieve remarkable success.

Responsibilities
  • Review data preparation tasks, and plans to address patterns or anomalies, while ensuring data readiness for advanced modeling and AI.
  • Review models for complex use cases (e.g., forecasting models, LLM-based solutions), and refine algorithms to meet business needs.
  • Review plan for smooth deployment into scalable, production-ready solutions.
  • Review test plans and test results for analytics use cases, while defining optimization standards for model accuracy and stability, in alignment with business goals.
  • Build models and analytics solutions tailored to business needs.
  • Ensure quality and scalability across client engagements while actively contributing to knowledge assets and innovation streams.
  • Leverage tools like SAS and R/Python to create reusable customizations for non-ML, ML, and deep learning algorithms, while enhancing analytics including LLMs, and create innovative, cost-effective solutions.
  • Review and refine analytics problems; identify data sources and extract from diverse environments.
  • Oversee analysis execution and drive business insights.
  • Create monitoring strategies across multiple projects, embedding governance frameworks to ensure robustness, reliability, and risk awareness.
  • Review monitoring frameworks, refine documentation/reporting templates, and present insights on anomalies or slippages to stakeholders.
  • Refine documentation strategy across teams, ensuring transparency and reproducibility of complex analytics solutions.
  • Collaborate with cross-functional teams, ensuring alignment between analytics delivery and business strategy.
  • Review analytics outputs for adherence to quality frameworks and project commitments.
  • Recommend improvements to quality metrics and guide team members to align with standards.
  • Identify and recommend model changes needed for successful deployment.
  • Engage in creation and refinement of IP assets such as analytics prototypes and accelerators.
  • Develop insights, whitepapers, and proof-of-concept summaries that highlight innovative thinking.
  • Review innovative models and applications in non-ML, ML, deep learning, or LLM areas.
  • Support participation in forums and internal knowledge exchanges.
  • Deliver training sessions on technical and analytics-specific topics.
  • Collaborate on content creation and mentor team members through hands‑on guidance in live projects.
  • Provide input for segment and unit-level business plans.
Your Contribution to the Team
  • A strong focus on innovation and scalable analytics solutions.
  • Proactive problem-solving ability for complex, data‑driven business challenges.
  • Deep technical expertise across advanced modeling and AI use cases.
  • A strategic mindset to align analytics with business goals.
  • Ability to mentor team members and drive continuous improvement.
  • Strong communication and knowledge‑sharing capabilities.
Required Skill and Experience
  • Architect and implement production‑grade AI agent solutions on Google Cloud Platform (GCP), with Azure as a supporting cloud environment where required.
  • Design end‑to‑end AI systems including:
    • Agent orchestration
    • Short‑term memory
    • Long‑term memory
    • Context management
    • Tool integrations
    • Workflow execution
    • Evaluation pipelines
  • Build and standardize architecture for production‑ready AI agents, ensuring scalability, resilience, security, and maintainability.
  • Define and implement AI Gateway patterns for model access, routing, authentication, rate limiting, and policy enforcement.
  • Design and deploy guardrails for responsible AI, safety, compliance, prompt protection, hallucination mitigation, and output validation.
  • Establish tracing and observability frameworks for AI applications using GCP‑native monitoring capabilities and Dynatrace.
  • Implement evaluation services for AI systems, including:
    • Offline evaluation
    • Online evaluation
    • Model and agent performance benchmarking
    • Quality and reliability measurement
  • Define and implement architecture for memory systems, including short‑term conversational memory and persistent long‑term memory.
  • Collaborate with engineering, data, product, security, and customer stakeholders to align AI solutions with business and technical requirements.
  • Lead technical discussions with different customer stakeholders, translating business needs into scalable AI architectures.
  • Drive architecture decisions under aggressive timelines while maintaining delivery quality and engineering rigor.
  • Ensure AI systems adhere to enterprise standards for governance, privacy, security, compliance, and operational excellence.
  • Provide technical leadership, mentorship, and architecture guidance to cross‑functional teams.
Preferred Skill and Experience
  • Experience working in onshore/offshore delivery model.
Additional Required Qualifications
  • Bachelor’s degree or foreign equivalent required from an accredited institution. Will also consider three years of progressive experience in the specialty in lieu of every year of education.
  • This position may require relocation and/or travel to work/project location.
  • Candidates authorized to work for any employer in the United States without employer‑based visa sponsorship are welcome to apply. Infosys is unable to provide immigration sponsorship for this role now or in the future.
Benefits
  • Long‑term/Short‑term Disability
  • Health and Dependent Care Reimbursement Accounts
  • Insurance (Accident, Critical Illness, Hospital Indemnity, Legal)
  • 401(k) plan and contributions dependent on salary level
About Us

Infosys is a global leader in next‑generation digital services and consulting. We enable clients in more than 50 countries to navigate their digital transformation. With over four decades of experience in managing the systems and workings of global enterprises, we expertly steer our clients through their digital journey. We do it by enabling the enterprise with an AI‑powered core that helps prioritize the execution of change. We also empower the business with agile digital at scale to deliver unprecedented levels of performance and customer delight. Our always‑on learning agenda drives their continuous improvement through building and transferring digital skills, expertise, and ideas from our innovation ecosystem.

EEO

Infosys provides equal employment opportunities to applicants and employees without regard to race; color; sex; gender identity; sexual orientation; religious practices and observances; national origin; pregnancy, childbirth, or related medical conditions; status as a protected veteran or spouse/family member of a protected veteran; or disability.

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