machine learning infrastructure engineer

Mount Sinai Health System

<div>Description
This position is Onsite/Hybrid - requires 1 day a week onsite
Location: 150 E 42nd Street, New York, NY
Acts as the principal technology expert for major systems and their subsystems utilizing a thorough understanding of available technology, tools and existing designs. 
Responsibilities<ul><li>
Administer and maintain Linux server infrastructure, including system configuration, user access management, and monitoring to ensure reliability and performance.</li><li>
Manage Microsoft Azure cloud resources, including security configuration using Azure Role-Based Access Control (RBAC) and identity management through Azure Active Directory.</li><li>
Develop and maintain CI/CD pipelines using Docker, Kubernetes, Jenkins, and Bitbucket for automated deployment and integration.</li><li>
Build and support big data infrastructure using technologies such as Apache Kafka, Apache Spark, MongoDB, and Splunk.</li><li>
Deploy, maintain, and monitor machine learning applications, including recommendation systems and deep learning&ndash;based classifiers.</li><li>
Implement infrastructure monitoring, logging, and debugging solutions using tools such as Splunk, Plotly, and Nagios.</li><li>
Prepare periodic reports on infrastructure performance, system health, and machine learning application operations.</li><li>
Develop prototypes and proof-of-concept solutions and implement real-time machine learning infrastructure for advanced use cases.</li></ul> 

Qualifications<ul><li>
Bachelor degree in Computer Science or a related discipline, or an equivalent combination of education and work experience. Masters degree preferred.</li><li>
Eight years of diverse work experience in IT with a minimum of six years experience in systems analysis and application program development, or an equivalent combination of education and work experience.</li></ul>
Strongly Preferred:<ul><li>
Minimum 2 years real world DevOps and Programming experience building and maintaining complex systems, and automating, installing and configuration of software</li><li>
Knowledge of configuring and managing Linux and Window Servers</li><li>
Knowledge of configuring and managing database system like, PostgreSQL and MySQL.</li><li>
Knowledge of Managing Cloud environment like Azure and AWS.</li><li>
Detailed working knowledge of networking and control access to hosting environments</li><li>
Working knowledge of Ansible and other dev-ops automation tools</li><li>
 Ability to use a wide variety of open source technologies.</li></ul> 
Compensation Statement 
The Mount Sinai Health System (MSHS) provides salary ranges that comply with the New York City Law on Salary Transparency in Job Advertisements. The salary range for the role is $109,000.00 - $163,695.00 Annually. Actual salaries depend on a variety of factors, including experience, education, and operational need. The salary range or contractual rate listed does not include bonuses/incentive, differential pay or other forms of compensation or benefits.
Non-Bargaining Unit, 271 - DTP Clinical Data Science - MSH, Mount Sinai Hospital 
Employer Description
Strength through Unity and Inclusion

The Mount Sinai Health System is committed to fostering an environment where everyone can contribute to excellence. We share a common dedication to delivering outstanding patient care. When you join us, you become part of Mount Sinai&rsquo;s unparalleled legacy of achievement, education, and innovation as we work together to transform healthcare. We encourage all team members to actively participate in creating a culture that ensures fair access to opportunities, promotes inclusive practices, and supports the success of every individual.

At Mount Sinai, our leaders are committed to fostering a workplace where all employees feel valued, respected, and empowered to grow. We strive to create an environment where collaboration, fairness, and continuous learning drive positive change, improving the well-being of our staff, patients, and organization. Our leaders are expected to challenge outdated practices, promote a culture of respect, and work toward meaningful improvements that enhance patient care and workplace experiences. We are dedicated to building a supportive and welcoming environment where everyone has the opportunity to thrive and advance professionally. Explore this opportunity and be part of the next chapter in our history.

About the Mount Sinai Health System:

Mount Sinai Health System is one of the largest academic medical systems in the New York metro area, with more than 48,000 employees working across eight hospitals, more than 400 outpatient practices, more than 300 labs, a school of nursing, and a leading school of medicine and graduate education. Mount Sinai advances health for all people, everywhere, by taking on the most complex health care challenges of our time &mdash; discovering and applying new scientific learning and knowledge; developing safer, more effective treatments; educating the next generation of medical leaders and innovators; and supporting local communities by delivering high-quality care to all who need it. Through the integration of its hospitals, labs, and schools, Mount Sinai offers comprehensive health care solutions from birth through geriatrics, leveraging innovative approaches such as artificial intelligence and informatics while keeping patients&rsquo; medical and emotional needs at the center of all treatment. The Health System includes more than 9,000 primary and specialty care physicians; 13 joint-venture outpatient surgery centers throughout the five boroughs of New York City, Westchester, Long Island, and Florida; and more than 30 affiliated community health centers. We are consistently ranked by U.S. News &amp; World Report's Best Hospitals, receiving high &quot;Honor Roll&quot; status, and are highly ranked: No. 1 in Geriatrics, top 5 in Cardiology/Heart Surgery, and top 20 in Diabetes/Endocrinology, Gastroenterology/GI Surgery, Neurology/Neurosurgery, Orthopedics, Pulmonology/Lung Surgery, Rehabilitation, and Urology. New York Eye and Ear Infirmary of Mount Sinai is ranked No. 12 in Ophthalmology. U.S. News &amp; World Report&rsquo;s &ldquo;Best Children&rsquo;s Hospitals&rdquo; ranks Mount Sinai Kravis Children's Hospital among the country&rsquo;s best in several pediatric specialties. The Icahn School of Medicine at Mount Sinai is ranked No. 11 nationwide in National Institutes of Health funding and in the 99th percentile in research dollars per investigator according to the Association of American Medical Colleges. Newsweek&rsquo;s &ldquo;The World&rsquo;s Best Smart Hospitals&rdquo; ranks The Mount Sinai Hospital as No. 1 in New York and in the top five globally, and Mount Sinai Morningside in the top 20 globally.

Equal Opportunity Employer

The Mount Sinai Health System is an equal opportunity employer, complying with all applicable federal civil rights laws. We do not discriminate, exclude, or treat individuals differently based on race, color, national origin, age, religion, disability, sex, sexual orientation, gender, veteran status, or any other characteristic protected by law. We are deeply committed to fostering an environment where all faculty, staff, students, trainees, patients, visitors, and the communities we serve feel respected and supported. Our goal is to create a healthcare and learning institution that actively works to remove barriers, address challenges, and promote fairness in all aspects of our organization. 
Compensation
The Mount Sinai Health System (MSHS) provides salary ranges that comply with the New York City Law on Salary Transparency in Job Advertisements. The salary range for the role is $109000 - $163695 Annually. Actual salaries depend on a variety of factors, including experience, education, and operational need. The salary range or contractual rate listed does not include bonuses/incentive, differential pay or other forms of compensation or benefits.</div>

DevOps Engineer (Technology Specialist I) - Digital and Technology Partners - Onsite/Hybrid

<div>Overview:
<div>
WHAT YOU DO AT AMD CHANGES EVERYTHING</div><div>
At AMD, our mission is to build great products that accelerate next-generation computing experiences&mdash;from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you&rsquo;ll discover the real differentiator is our culture. We push the limits of innovation to solve the world&rsquo;s most important challenges&mdash;striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. Together, we advance your career.</div>
Responsibilities:
<div>
THE ROLE:</div><div></div><div> 
You will be part of a small, but dedicated team driving discrete GPU products&rsquo; performance attainment solutions across hardware, software and the platform. We are seeking a highly skilled engineer to join our Infrastructure team, focused on building scalable solutions for workload automation and performance analysis supporting advanced machine learning workloads.</div><div> 
</div><div>THE PERSON:</div><div>
You have strong backgrounds in Python development, Linux system administration, and build toolchain expertise.</div><div>
You have experience building or using internal workflow orchestration platforms to manage and execute distributed or multi-system compute workloads</div><div>
You have familiarity with server hardware, networking protocols, and mixed hardware/software environments</div><div>
You can adapt quickly to new tools and technologies and apply them effectively.</div><div>
You can manage multiple tasks, priorities, and release cycles in parallel.</div><div></div><div> 
KEY RESPONSIBILITIES:</div><ul><li>
Technical team lead for a team of 5-6 engineers</li><li>
Assess and understand the current automation and performance analysis infrastructure, identifying strengths, gaps, and opportunities for improvement.</li><li>
Collaborate with internal teams to gather technical requirements and understand evolving needs.</li><li>
Develop a forward looking plan that balances reusing existing systems with building new infrastructure where appropriate.</li><li>
Design, develop, and maintain automation and performance analysis tooling using Python, Bash, Make, and related technologies.</li><li>
Build and enhance workflow automation solutions using internally developed tools to orchestrate ML workloads.</li><li>
Develop new techniques and tooling to optimize ML workload execution, profiling, and analysis at scale.</li></ul><div></div><div> 
PREFERRED EXPERIENCE:</div> 
<ul><li>
</li><li>
Strong development experience in Python and/or Bash (or equivalent scripting languages).</li></ul><ul><li>
Experience with Github, Jenkins, or similar CI/CD and code review systems.</li><li>
 Linux system administration experience preferred.</li><li>
 Experience developing automated test infrastructure and orchestrating multisystem workflows is preferred. Ansible experience is a bonus.</li><li>
Strong analytical, problem solving, and debugging skills</li><li>
 Excellent communication skills; must be a critical thinker and self-starter.</li><li>
Ability to quickly learn and apply new tools, technologies, and frameworks.</li><li>
Networking experience preferred, including common protocols and basic debugging.</li><li>
Experience with Docker/containers and/or virtualization technologies preferred</li><li>
Motivating leader with good interpersonal skills</li></ul><div></div><div> 
ACADEMIC CREDENTIALS:</div><ul><li><ul><li>Bachelor&rsquo;s degree in a Computer Engineering/Computer Science field with 9+ years of hands-on experience, or a Master&rsquo;s degree with 7+ years of relevant experience.</li></ul></li></ul><div></div><div> 
This role is not eligible for visa sponsorship.</div><div></div><div> 
#LI-RL1</div>
Qualifications:
<div>
Benefits offered are described: AMD benefits at a glance.</div><div></div><div> 
AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants&rsquo; needs under the respective laws throughout all stages of the recruitment and selection process.</div><div></div><div> 
AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD&rsquo;s &ldquo;Responsible AI Policy&rdquo; is available here.</div><div></div><div> 
This posting is for an existing vacancy.</div></div>

Performance Infrastructure Engineer- Data Center GPU

Amazon Web Services

<div><h2 class="jobSectionHeader">DESCRIPTION</h2><div>
Annapurna Labs designs silicon and software that accelerates innovation. Customers choose us to create cloud solutions that solve challenges that were unimaginable a short time ago- even yesterday. Our custom chips, accelerators, and software stacks enable us to take on technical challenges that have never been seen before, and deliver results that help our customers change the world. 
 
In Annapurna Labs we are at the forefront of hardware/software co-design not just in Amazon Web Services (AWS) but across the industry. Our Annapurna MLA Software team is looking for candidates interested in diving deep into the different hardware technologies that power our Machine Learning servers and develop the software and firmware to drive, support and sustain these technologies as they evolve though concept and manufacturing, and finally take their place in our rapidly expanding fleet of bleeding edge Machine Learning products our customers demand. 
 
Key job responsibilities 
<ul><li>Architect and develop mission-critical software that powers Annapurna Labs' next-generation machine learning platforms</li><li>Collaborate with EC2 teams and manufacturing partners to ensure seamless system integration</li><li>Drive end-to-end qualification processes for new software implementations</li><li>Craft high-performance solutions using C/C++ running on Linux</li></ul> 
A day in the life 
You'll design and develop the software and firmware that drives NeuronSwitches- the high-performance interconnect fabric for Trainium chips. Day-to-day, you'll work closely with EC2, Annapurna Labs teams and manufacturing teams to bring up new hardware, debug board-level issues and optimize data paths. You'll write device drivers, build ML infrastructure, implement switch fabric control logic and develop the tooling needed for testing, qualification, and production deployment. 
This is hands-on systems work- from initial hardware bring-up through manufacturing scale-up. 
 
About the team 
The Annapurna ML Software team builds the software and firmware that powers NeuronSwitches- AWS' next-generation switching infrastructure forming the high-performance interconnect fabric for Trainium 3 
chips. We focus on mission-mode control of sensors, board-level hardware, and the critical data paths that enable chip-to-chip communication at scale. Our work spans device drivers, switched fabric and everything in between- from debug and testing through qualification and manufacturing. 
 
We work at the hardware-software boundary where silicon meets systems. While ML engineers optimize models and algorithms, we ensure the underlying infrastructure can move data at the 
speeds those workloads demand. If you're excited about low-level systems programming, hardware bring-up, and building the foundation that makes next generation AI possible, this is the team 
for you.</div><h2 class="jobSectionHeader">
BASIC QUALIFICATIONS</h2><ul><li>3+ years of non-internship professional software development experience</li><li>2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience</li><li>Experience programming with at least one software programming language</li></ul><h2 class="jobSectionHeader">
PREFERRED QUALIFICATIONS</h2><ul><li>3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience</li><li>Bachelor's degree in computer science or equivalent</li></ul> 
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status. 
 
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company&rsquo;s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records. 
 
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you&rsquo;re applying in isn&rsquo;t listed, please contact your Recruiting Partner. 
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life &amp; AD&amp;D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits. 
USA, CA, Cupertino - 165,200.00 - 223,600.00 USD annually 
USA, TX, AUSTIN - 143,700.00 - 194,400.00 USD annually 
USA, TX, Austin - 143,700.00 - 194,400.00 USD annually</div>

Embedded Software Engineer, Annapurna ML Software

General Motors (GM)

<div>Job Description
The Role
The Senior Data Engineer, AI and Robotics will join the AI Research team within the Autonomous Robotics Center (ARC). This role owns the robotics data backbone that enables scalable robot learning in manufacturing &mdash; from data capture and curation through versioning, serving, and auditing. Your work will make model development reproducible, testable, and production-ready.
This is a robotics and machine learning (ML) infrastructure role, focused on multimodal robotic datasets and continuous model iteration. You will partner closely with AI researchers, robotics engineers, and plant teams to turn real-world robot behavior and failures into high-quality training data and robust production systems.
What You&rsquo;ll Do
<ul><li>Build and operate multimodal data pipelines for robotics (e.g., vision, depth, force/torque, joint states, events, metadata), including reliable capture from lab and plant-adjacent cells.
</li><li>Implement reproducible data logging and replay workflows (including ROS 2 bagging where applicable) to enable debugging, regression testing, and dataset construction.
</li><li>Own dataset lifecycle management, including versioning and lineage, provenance, governance, and data quality gates to support trustworthy model training and evaluation.
</li><li>Integrate experiment tracking and model/data traceability so teams can compare runs, reproduce results, and audit changes over time.
</li><li>Establish MLOps automation patterns (CI/CD/CT-style pipelines for ML) that reduce manual toil and increase deployment confidence for robotics AI updates.
</li><li>Partner with AI/ML, planning, and validation teams to define data contracts (schemas, labeling standards, failure taxonomies) and turn field failures into curated training datasets.
</li></ul>What You&rsquo;ll Need (Required Qualifications)
<ul><li>B.S. or M.S. in Computer Science, Computer Engineering, Data Engineering, or a related field.
</li><li>6+ years of experience building production data systems and/or ML infrastructure, with practical experience supporting training pipelines end-to-end.
</li><li>Strong proficiency in Python and at least one of: C++, Scala, or Java.
</li><li>Demonstrated engineering discipline in testing, documentation, and operational reliability.
</li><li>Experience with dataset versioning, lineage, and reproducibility tooling (e.g., DVC or equivalent approaches).
</li><li>Experience with experiment tracking and model registry patterns (e.g., MLflow or equivalent tools).
</li><li>Ability to work onsite with hardware and robotics teams, and to design pipelines that handle real-world robotic logging constraints (e.g., bandwidth limits, dropped frames, timing drift).
</li></ul>What Will Give You a Competitive Edge (Preferred Qualifications)
<ul><li>Hands-on robotics logging and replay experience (e.g., ROS 2 bags, system telemetry pipelines).
</li><li>Experience with simulation-to-real data workflows and dataset synthesis strategies.
</li><li>Familiarity with data governance requirements and auditability in safety-adjacent or safety-critical systems.
</li><li>Experience building tools to support data labeling workflows, quality assurance, and active learning loops.
</li></ul>&amp;#xa;&amp;#xa;&amp;#xa;&amp;#xa;
About GM
Our vision is a world with Zero Crashes, Zero Emissions and Zero Congestion and we embrace the responsibility to lead the change that will make our world better, safer and more equitable for all.
Why Join Us
We believe we all must make a choice every day &ndash; individually and collectively &ndash; to drive meaningful change through our words, our deeds and our culture. Every day, we want every employee to feel they belong to one General Motors team.
Benefits Overview
From day one, we're looking out for your well-being&ndash;at work and at home&ndash;so you can focus on realizing your ambitions. Learn how GM supports a rewarding career that rewards you personally by visiting Total Rewards resources .
Non-Discrimination and Equal Employment Opportunities (U.S.)
General Motors is committed to being a workplace that is not only free of unlawful discrimination, but one that genuinely fosters inclusion and belonging. We strongly believe that providing an inclusive workplace creates an environment in which our employees can thrive and develop better products for our customers.
All employment decisions are made on a non-discriminatory basis without regard to sex, race, color, national origin, citizenship status, religion, age, disability, pregnancy or maternity status, sexual orientation, gender identity, status as a veteran or protected veteran, or any other similarly protected status in accordance with federal, state and local laws.
We encourage interested candidates to review the key responsibilities and qualifications for each role and apply for any positions that match their skills and capabilities. Applicants in the recruitment process may be required, where applicable, to successfully complete a role-related assessment(s) and/or a pre-employment screening prior to beginning employment. To learn more, visit How we Hire .
Accommodations
General Motors offers opportunities to all job seekers including individuals with disabilities. If you need a reasonable accommodation to assist with your job search or application for employment, email us or call us at 1-800-865-7580. In your email, please include a description of the specific accommodation you are requesting as well as the job title and requisition number of the position for which you are applying.</div>

Senior Data Engineer, AI&Robotics

Recutify Inc.

Role: AI Platform Engineer (Guardrails, Observability & Evaluation Infrastructure)
 
Position: Contract
 
Location: Charlotte NC (100% onsite)
 
Job Description: 
AI Platform Engineer to design and build the foundational components that power enterprise-scale GenAI Applications. This includes data guardrails, model safety tooling, observability pipelines, evaluation harnesses, and standardized logging/monitoring frameworks. This role is critical for enabling safe, reliable, and compliant AI development across multiple use cases, teams, and business units. Idea is to create the common platform services that AI team will build upon.
 
Key Responsibilities
 
1. Guardrails, Safety & Governance
 Design and implement data guardrail frameworks (pre-processing, redaction, PII/PHI filtering, DLP integration, prompt defenses).
 
Build "Model Armor" components such as: 
Input validation & sanitization
 Prompt-injection defenses
 Harmful content detection & policy enforcement
 Output filtering, factchecking, grounding checks
 
Integrate safety tooling (policy engines, classifiers, DLP APIs/safety models).
 
Collaborate with Security, Compliance, and Data Privacy teams to ensure frameworks meet enterprise governance requirements.
 
2. Observability Frameworks
 
Build and maintain observability pipelines using tools like Arize AI (tracing, quality metrics, dataset drift/hallucination tracking, embedding monitoring).
 
Define and enforce platform-wide standards for: 
Tracing LLM calls
 Token usage and cost monitoring
 Latency and reliability metrics
 Prompt/model version tracking
 
Provide reusable SDKs or middleware for engineering teams to adopt observability with minimal friction.
 
3. Logging, Monitoring & Telemetry
 
Design standardized LLM-specific logging schemas, including: 
Inputs/outputs
 Model metadata
 Retrieval metadata
 Safety flags
 User context and attribution
 
Build monitoring dashboards for performance, cost, anomalies, errors, and safety events.
 
Implement alerting and SLOs/SLIs for LLM inference systems.
 
4. Evaluation Infrastructure
 
Architect and maintain evaluation harnesses for GenAI systems, including:
 RAG evaluation (faithfulness, relevance, hallucination risk)
 Summarization/QA evaluation
 Human-in-the-loop review workflows
 Automated eval pipelines integrated into CI/CD
 
Support frameworks such as RAGAS, G-Eval, rubric scoring, pairwise comparisons, and test case generation.
 
Build reusable tooling for teams to write, run, and track model evaluations.
 
5. Platform Engineering & Reusable Components
 
Develop shared libraries, APIs, and services for: 
Prompt management/versioning
 Embedding pipelines and model wrappers
 Retrieval adapters
 Common data loaders and document preprocessing
 Tool/function schemas
 
Drive consistency across teams with standards, reference architectures, and best practices.
 
Review system designs across use cases to ensure alignment to platform patterns.
 
6. Collaboration & Enablement
 Partner with AI engineers, product teams, and data scientists to understand cross-cutting needs and convert them into reusable platform features.
 Create documentation, onboarding guides, examples, and developer tooling.
 Provide internal training (brown bags, workshops) on guardrails, observability, and evaluation frameworks.
 
Required Qualifications Technical Skills
 
5-10+ years software engineering or ML infrastructure experience.
 Strong Python engineering fundamentals (FastAPI, async, typing/Pydantic, testing).
 Experience with model safety/guardrails approaches (prompt injection defense, PII redaction, toxicity filters, policy enforcement).
 Hands-on with Arize AI, LangSmith, or similar LLM observability platforms.
 Experience creating evaluation frameworks using RAGAS, G-Eval, or custom rubric systems.
 Strong familiarity with vector databases (Pinecone, Weaviate, Milvus), embeddings, and retrieval pipelines.
 Solid understanding of LLM architectures, tokenization, embeddings, context limits, and RAG patterns.
 Experience in cloud (GCP preferred), Kubernetes/GE, containers, and CI/CD.
 Strong understanding of security, governance, DLP, data privacy, RBAC, and enterprise compliance requirements.
 
Soft Skills
 
Strong documentation and communication skills.
 Ability to influence engineering teams and standardize best practices.
 Comfortable working across multiple stakeholders-platform, security, ML engineering, product.
 
Nice to Have
 
Experience with LangChain/LangGraph or Llamalndex orchestrations.
 Experience with Guardrails.ai, Rebuff, Protect AI, or similar LLM security tooling.
 Experience with GCP Vertex AI pipelines, Model Monitoring, and Vector Search.
 Familiarity with knowledge graphs, grounding models, fact-checking models.
 Building SDKs or developer frameworks adopted across multiple teams.
 On-prem or hybrid AI deployment experience.

AI Platform Engineer

Atlas Roofing Corporation

<div>Job Title: Data Engineer Co-op
 
Location: Meridian, MS
 
Duration: One term Co-op starting in July 2026
 
Department: Engineering / Operations
 
Reports To: Nicki Vaughn, Plant Manager
We are seeking a skilled Data Engineer Intern for our Meridian, MS, Web Technologies plant with strong experience in machine learning frameworks, industrial automation, and database technologies. This role will focus on designing, building, and maintaining data pipelines, machine learning solutions, and automation scripts that support analytics, operational efficiency, and system integration&mdash;particularly within industrial or manufacturing environments.
Data Engineer Co-op Key Responsibilities
<ul><li>Design, develop, and deploy machine learning models to support predictive analytics, classification, and regression use cases.
</li><li>Build and maintain data pipelines that integrate machine learning outputs into operational systems.</li>
<li>Develop and maintain Ignition (Inductive Automation) scripts to automate workflows, trigger actions, and integrate data across systems.
</li><li>Work with Ignition modules such as Vision, Perspective, and SQL Bridge to support SCADA and industrial automation solutions.
</li><li>Write, optimize, and maintain complex SQL queries, stored procedures, views, and triggers in MS SQL and Oracle databases.
</li><li>Collaborate with cross-functional teams (engineering, operations, IT) to integrate data and automation solutions into existing infrastructure.
</li><li>Ensure data quality, performance, and reliability across databases and analytics platforms.
</li></ul>Data Engineer Co-op Required Skills &amp; Qualifications
<ul><li>Second-semester freshman standing or higher
</li><li>2 GPA or higher
</li></ul>Machine Learning &amp; Analytics
<ul><li>Experience with machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, or Keras.</li>
<li>Strong understanding of model training, evaluation, and optimization techniques (e.g., cross-validation, hyperparameter tuning).</li>
<li>Ability to deploy machine learning models into production environments.</li>
</ul>
Automation &amp; Ignition
<ul><li>Hands-on experience with Ignition (Inductive Automation), including scripting and automation.</li>
<li>Proficiency in Ignition scripting using Python (Jython).
</li><li>Experience in integrating Ignition with databases and external systems.</li>
</ul>
Programming &amp; Databases
<ul><li>Strong proficiency in Python for data processing, automation, and machine learning.</li>
<li>Experience using R for statistical analysis and data visualization.</li>
<li>Advanced SQL skills with MS SQL and Oracle SQL.
</li><li>Solid understanding of relational database design, data modeling, and performance tuning.</li>
</ul>
Data Engineer Co-op Preferred Qualifications
<ul><li>Experience in industrial, manufacturing, or automation-focused environments.
</li><li>Familiarity with SCADA systems and operational technology (OT) data.</li>
<li>Experience integrating analytics or machine learning solutions into real-time systems.
</li></ul>What We Offer
<ul><li>Opportunity to work on data-driven automation and machine learning solutions.</li>
<li>Collaborative, cross-functional work environment
</li><li>Exposure to cutting-edge industrial analytics and automation technologies
</li></ul>Atlas Total Compensation
<ul><li>Hourly Wages
</li><li>Second Semester Freshman: $18-19/hr.
</li><li>Sophomore: $19-21/hr.
</li><li>Jr: $20-23/hr.
</li><li>Senior: $21-25/hr.
</li><li>Housing Assistance Provided
</li></ul>Resume Submittal
<ul><li>Interested? To submit your resume, scan below.</li></ul></div>

Data Engineer Co-op

The Home Depot

<div><div>Position Purpose:
The Staff Machine Learning Engineer is responsible for joining cross-functional teams (product, design, Engineering, infrastructure) to build innovative GenAI application experiences and overall product lifecycle for a product that our users love. The engineering process is highly collaborative. Staff ML Engineers are expected to pair daily as they work through user stories and support products as they evolve. 
Generative AI Engineers may design and implement applications using large language models (LLMs) and other generative models to embed intelligent capabilities directly into software products. Activities may include prompt engineering, model integration, building Retrieval-Augmented Generation (RAG) pipelines, and developing scalable AI services. The role may interact with business stakeholders, infrastructure teams, and development teams to ensure business requirements are effectively addressed through generative AI solutions. The role may also support evaluation, performance optimization, testing, and monitoring of AI systems in production. Additional responsibilities may include working with domain data, improving prompts and AI workflows, and creating documentation or enablement materials for generative AI solutions. 
Key Responsibilities:<ul><li>
45% Delivery and Execution - Collaborates and pairs with other product team members (UX, engineering, and product management) to create secure, reliable, scalable machine learning solutions, Works with Product Team to ensure user stories that are developer-ready, easy to understand, and testable; Configures commercial off the shelf solutions to align with evolving business needs; Creates meaningful dashboards, logging, alerting, and responses to ensure that issues are captured and addressed proactively</li><li>
15% Learning - Participates in learning activities around modern software design, machine learning, and development core practices (communities of practice); Proactively views articles, tutorials, and videos to learn about new technologies and best practices being used within other technology organizations; Attends conferences and learns how to apply new innovations and technologies where appropriate</li><li>
20% Strategy and Planning - Researches and analyzes business trends and behavioral data to identify opportunities for improvement and new initiatives; Leads the evaluation development and recommendation of specific technology products and platforms to provide cost-effective solutions that meet business and technology requirements; Researches and designs best fit infrastructure, network, database, security, and machine learning architectures for products; Proactively creates and maintains tools for monitoring and support; Participates in project planning and management across multiple efforts; Develops formal training courses</li><li>
20% Support and Enablement - Fields questions from other product teams or support teams; Monitors tools and participates in conversations to encourage collaboration across product teams; Provides application support for software running in production; Proactively monitors production Service Level Objectives for products; Proactively reviews the Performance and Capacity of all aspects of production: code, infrastructure, data, message processing, and prediction quality</li></ul> 
Direct Manager/Direct Reports:<ul><li>
This Position typically reports to Software Engineer Manager or Sr Software Engineer Manager</li><li>
This Position has 0 Direct Reports</li></ul> 
Travel Requirements:<ul><li>
Typically requires overnight travel 5% to 20% of the time.</li></ul> 
Physical Requirements:<ul><li>
Most of the time is spent sitting in a comfortable position and there is frequent opportunity to move about. On rare occasions there may be a need to move or lift light articles.</li></ul> 
Working Conditions:<ul><li>
Located in a comfortable indoor area. Any unpleasant conditions would be infrequent and not objectionable.</li></ul> 
Minimum Qualifications:<ul><li>
Must be eighteen years of age or older.</li><li>
Must be legally permitted to work in the United States.</li></ul> 
Preferred Qualifications:<ul><li>
5 - 7 years of relevant work experience</li><li>
Experience in Python and modern AI development frameworks</li><li>
Experience building Generative AI applications using large language models (LLMs)</li><li>
Experience with prompt engineering, prompt optimization, and prompt evaluation techniques</li><li>
Experience integrating AI models through APIs from platforms such as Google, OpenAI or Anthropic</li><li>
Experience with GenAI frameworks such as Google Agent Development Kit (ADK)</li><li>
Experience implementing Retrieval-Augmented Generation (RAG) pipelines using vector databases</li><li>
Experience working with vector databases such as google Vertex AI Search</li><li>
Experience with building conversational AI systems, or AI assistants</li><li>
Experience with responsible AI practices including bias mitigation and safety guardrails</li><li>
Experience working with graph databases, knowledge ingestion pipelines, and data mesh architectures to enable scalable, connected, and queryable AI knowledge systems.</li><li>
Experience implementing CI/CD pipelines, monitoring, and automated workflows for reliable AI model deployment and lifecycle management.</li><li>
Experience with monitoring, evaluation, and optimization of production AI systems</li><li>
Experience in Google Cloud Platform and AI/ML related components such as Vertex AI, BigQueryML, and Experience in effective data engineering practices and big data platforms such as BigQuery, Data Store, etc- Experience in a modern scripting language (preferably Python)</li><li>
Experience with GPU acceleration (i.e. CUDA and cuDNN)</li><li>
Experience in a front-end technology and framework such as Node.js, HTML, CCS, JavaScript, ReactJS, D3</li><li>
Experience in writing SQL queries against a relational database</li><li>
Experience in advanced machine learning techniques such as NLP, convolutional neural networks, autoencoders, and embeddings generation and utilization</li><li>
Experience in training machine learning models with extremely large datasets</li><li>
Experience with Data Analysis and Machine Learning Tools and Libraries like Jupyter Notebooks, Pandas, SciPy, Scikit-learn, Gensim, tensorflow, pytorch, etc.</li><li>
Familiarity with production systems design including High Availability, Disaster Recovery, Performance, Efficiency, and Security</li><li>
Familiarity with cloud computing platform and associated automation patterns and machine learning services they provide</li><li>
Familiarity with defensive coding practices and patterns for high Availability</li><li>
Familiarity with A/B testing and effective REST design for scalable web services architecture</li><li>
Familiarity with advanced machine learning techniques such as NLP, convolutional neural networks, autoencoders, and embeddings generation and utilization</li><li>
Familiarity with advanced machine learning architectures GANs, GRU, LSTMs, RNNs, CNNs, style transfer</li></ul> 
Minimum Education:<ul><li>
The knowledge, skills and abilities typically acquired through the completion of a high school diploma and/or GED.</li></ul> 
Preferred Education:<ul><li>
No additional education</li></ul> 
Minimum Years of Work Experience:<ul><li>
3</li></ul> 
Preferred Years of Work Experience:<ul><li>
No additional years of experience</li></ul> 
Minimum Leadership Experience:<ul><li>
None</li></ul> 
Preferred Leadership Experience:<ul><li>
None</li></ul> 
Certifications:<ul><li>
None</li></ul> 
Competencies:<ul><li>
Global Perspective</li><li>
Manages Ambiguity</li><li>
Nimble Learning</li><li>
Self-Development</li><li>
Collaborates</li><li>
Cultivates Innovation</li><li>
Situational Adaptability</li><li>
Communicates Effectively</li><li>
Drives Results</li><li>
Interpersonal Savvy</li></ul>
Benefits offered include health care benefits, 401K, ESPP, paid time off, and success sharing bonus. For a full list of the various benefits The Home Depot offers, visit https://careers.homedepot.com/our-benefits.</div> 
Apply End Date: 05/11/2026</div>

Staff Machine Learning Engineer – Generative AI (Remote)

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