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Deep Learning Accelerator Jobs in Georgia (NOW HIRING)

... our Honeywell Technologies Accelerator operating system and Honeywell Technologies Forge ... Minimum of 4 years of experience in deep learning frameworks like PyTorch, Tensorflow, Keras

... our Honeywell Technologies Accelerator operating system and Honeywell Technologies Forge ... Minimum of 4 years of experience in deep learning frameworks like PyTorch, Tensorflow, Keras

Minimum of 4 years of experience in deep learning frameworks like PyTorch, Tensorflow, Keras ... our Honeywell Technologies Accelerator operating system and Honeywell Technologies Forge ...

... Accelerator operating system and Honeywell Technologies Forge intelligence layer ... By combining the deep domain expertise of our more than 50,000 employees with decades of data from ...

Advanced Project Engineer

Duluth, GA ยท On-site +1

$112K - $139K/yr

... Accelerator operating system and Honeywell Technologies Forge intelligence layer ... By combining the deep domain expertise of our more than 50,000 employees with decades of data from ...

Lead Account Manager

Atlanta, GA ยท On-site

$144K - $180K/yr

... Accelerator operating system and Honeywell Technologies Forge intelligence layer ... By combining the deep domain expertise of our more than 50,000 employees with decades of data from ...

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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.

What cities in Georgia are hiring for Deep Learning Accelerator jobs?

Cities in Georgia with the most Deep Learning Accelerator job openings:

Infographic showing various Deep Learning Accelerator job openings in Georgia as of June 2026, with employment types broken down into 2% Internship, 5% As Needed, 86% Full Time, 5% Part Time, and 2% Summer. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Agentic AI / Machine Learning Architect - Senior Principal (Atlanta)

Atlanta, GA โ€ข On-site

Slalom
Business Management Consultingย โ€ขย 5 - 10K employees

Full-time

Medical, Dental, Vision, Life, Retirement

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


Job description

At Slalom, we co-create modern technology and software products with clients who are accelerating their digital transformation journeys. We blend design, product engineering, analytics, automation, and AI-native delivery to build intelligent products and platforms that can operate safely at enterprise scale. As an AI/ML Architect, youโ€™ll design and deliver production-grade AI systems that combine machine learning, generative AI, agentic workflows, modern data platforms, and cloud-native engineering across AWS, Azure, and Google Cloud. Youโ€™ll partner with clients to shape strategy, define secure and governed architectures, and move AI solutions from experimentation into reliable business operations.

What Youโ€™ll Do
  • Set technical direction for enterprise-scale AI systems spanning data products, retrieval pipelines, model orchestration, agentic workflows, evaluation, deployment, monitoring, optimization, and lifecycle management.
  • Define secure, scalable, cloud-native and hybrid reference architectures across AWS, Azure, and Google Cloud, including modern AI platform services such as Amazon Bedrock, Azure AI Foundry, Google Vertex AI, and enterprise data platforms.
  • Guide applied AI strategy and delivery across generative AI, agentic AI, multimodal AI, advanced RAG, knowledge assistants, prediction, optimization, computer vision, and decision-support use cases.
  • Lead enterprise adoption of production GenAI and agentic AI, including advanced RAG, tool/function calling, structured outputs, workflow orchestration, model routing, prompt and context engineering, memory patterns, and human-in-the-loop controls.
  • Establish AI evaluation, observability, and reliability standards, including offline test sets, automated evals, tracing, hallucination detection, quality scoring, latency/cost monitoring, feedback loops, and regression testing.
  • Champion Responsible AI, AI security, and governance-by-design practices, including explainability, privacy, bias mitigation, guardrails, data protection, threat modeling, access controls, auditability, and compliance alignment.
  • Evaluate emerging models, platforms, frameworks, standards, and deployment patterns, providing executiveโ€‘ready recommendations based on use case fit, enterprise readiness, cost, risk, and operational complexity.
  • Lead and mentor crossโ€‘functional delivery teams of data engineers, AI engineers, ML engineers, software engineers, architects, and consultants, ensuring consistent quality across complex programs.
  • Drive business development through proposals, executive client pitches, solution accelerators, reference architectures, technical points of view, and thought leadership.
  • Develop senior practitioners and practice capability, fostering a culture of continuous learning, engineering discipline, responsible innovation, and practical AI adoption across the AI/ML practice.
  • Help to hire, lead, mentor, and retain a highโ€‘performing, inclusive team of AI/ML engineers, architects, and data scientists. Set clear expectations, provide timely feedback, and create meaningful development and stretch opportunities.
What Youโ€™ll Bring
  • 9+ years of experience implementing ML/AI solutions in production, including classical ML, deep learning, generative AI, or agentic AI systems.
  • 5+ years of experience in professional consulting or IT services, with proven ability to lead complex clientโ€‘facing technical engagements.
  • Proven ability to design and govern production AI systems that combine models, data, retrieval, orchestration, APIs, security controls, observability, operating model, and user experience into an endโ€‘toโ€‘end enterprise architecture.
  • Deep expertise in modern GenAI patterns, including advanced RAG, embeddings, vector and hybrid search, reโ€‘ranking, knowledge graphs, tool/function calling, structured outputs, context engineering, and multimodal inputs.
  • Experience defining agentic AI architecture patterns, including singleโ€‘agent and multiโ€‘agent workflows, supervisor/worker patterns, state and memory management, workflow orchestration, human approval gates, and safe action execution.
  • Proficiency with modern AI engineering frameworks and tools such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, Haystack, CrewAI, Hugging Face, or comparable openโ€‘source and cloudโ€‘native frameworks.
  • Strong programming skills in Python and modern software engineering practices, with familiarity in APIs, eventโ€‘driven patterns, test automation, infrastructure as code, and scalable service design.
  • Proficiency in cloud AI/ML platforms and services such as Amazon Bedrock, AWS SageMaker, Azure AI Foundry, Azure Machine Learning, Google Vertex AI, and related model hosting, retrieval, agent, and evaluation capabilities.
  • Experience with enterprise data and AI ecosystems such as Databricks, Snowflake, Spark, Kafka, dbt, vector databases, lakehouse architectures, and modern data governance patterns.
  • Experience setting standards for MLOps, LLMOps, CI/CD, model and prompt versioning, automated evaluation, observability, containerization, Kubernetes, serverless deployment, and cost/performance optimization.
  • Strong understanding of AI architecture tradeoffs, including model selection, retrieval strategy, latency, accuracy, security, privacy, scalability, cost, vendor lockโ€‘in, and operating model implications.
  • Ability to communicate complex AI concepts to technical and nonโ€‘technical stakeholders, translating architecture choices into business value, delivery risk, governance requirements, and executiveโ€‘level decisions.
  • Experience managing senior delivery teams and shaping enterprise AI/ML roadmaps, reference architectures, implementation backlogs, governance models, and adoption plans for enterprise clients.
  • Strong problemโ€‘solving, critical thinking, and business acumen, with the judgment to distinguish viable production solutions from prototypeโ€‘only patterns and to guide clients through tradeoffs pragmatically.
About Us

Slalom is a fiercely human business and technology consulting company that leads with outcomes to bring more value, in all ways, always. From strategy through delivery, our agile teams across 52 offices in 12 countries partner with clients to coโ€‘create powerful customer experiences, modern ways of working, and meaningful impact.

What sets us apart? We believe work should be challenging and fulfilling, not perfect, but possible. Thatโ€™s why we prioritize purpose, flexibility, connection, and recognition, so our people can thrive and love what they do, most days.

Compensation and Benefits

Slalom prides itself on helping team members thrive in their work and life. As a result, Slalom is proud to invest in benefits that includemeaningful time off and paid holidays, parental leave, 401(k) with a match, a range of choices for highly subsidized health, dental, & vision coverage, adoption and fertility assistance, and short/long-term disability. We also offer yearly $350 reimbursement account for any wellโ€‘beingโ€‘related expenses, as well as discounted home, auto, and pet insurance.

For Boston, New York, Washington D.C.:

The targeted base salary pay range for a Sr. Principal is $215,000 to $275,000.

For Atlanta, Charlotte, Philadelphia, Miami:

The targeted base salary pay range for a Sr. Principal is $200,000 to $250,000.

In addition, individuals may be eligible for an annual discretionary bonus . Actual compensation will depend upon an individualโ€™s skills, experience, qualifications, location, and other relevant factors. The salary pay range is subject to change and may be modified at any time.

Wearecommittedtopaytransparencyandcompliancewithapplicablelaws.Ifyouhavequestionsorconcernsaboutthepayrangeorothercompensationinformationinthisposting,pleasecontactusat: peopleone@slalom.com . Pleasenote,thisrecipientisnotabletosupportrecruitmentinquiriesbeyondthispurpose.

EEO and Accommodations

Slalom is an equal opportunity employer and is committed to attracting, developing and retaining highly qualified talent who empower our innovative teams through unique perspectives and experiences. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteransโ€™ status, or any other characteristic protected by federal, state, or local laws. Slalom will also consider qualified applications with criminal histories, consistent with legal requirements. Slalom welcomes and encourages applications from individuals with disabilities. Reasonable accommodations are available for candidates during all aspects of these selection processes. Please advise the talent acquisition team or contact accomodationrequest@slalom.com if you require accommodations during the interview process.

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