mlops engineer

Capgemini

<div><div><div><div>Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you&rsquo;d like, where you&rsquo;ll be supported and inspired by a collaborative community of colleagues around the world, and where you&rsquo;ll be able to reimagine what&rsquo;s possible. Join us and help the world&rsquo;s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.
<div><div><div><h2 class="jobSectionHeader">Location</h2>
</div><div>San Francisco CA
</div></div><div><div><h2 class="jobSectionHeader">Your Role</h2>
</div><div>The GenAI Engineer / Data Scientist is responsible for designing, developing, deploying, and maintaining Generative AI (GenAI) and Predictive AI solutions that deliver business value. The role requires expertise in AI/ML, cloud platforms, APIs, data engineering, MLOps, and AI agent workflows, with a strong focus on production-grade AI implementation. 
 
Key Responsibilities 
 
Develop and deploy Generative AI solutions (GPT, Claude, Stable Diffusion) and Predictive AI models (regression, classification, clustering).
 
Design and integrate AI applications using REST APIs, authentication mechanisms, JSON services, and cloud-based architectures.
 
Build and support AI agents, autonomous systems, and agentic workflows.
 
Deploy, monitor, and optimize AI models in production using MLOps practices.
 
Design and maintain data pipelines, data lakes, and data warehouses.
 
Monitor model performance, troubleshoot issues, and manage data/model drift.
 
Collaborate with business and technical teams to align AI solutions with organizational objectives.
 
Create reusable assets, demos, and proof-of-concepts to support innovation and business development. 
 
 
</div></div><div><div><h2 class="jobSectionHeader">Required Skills and Experience
</h2></div><div>A hands-on AI professional with strong expertise in Generative AI, Machine Learning, Python, Cloud Technologies, APIs, MLOps, Data Engineering, and AI Agents, capable of building and scaling enterprise-grade AI solutions from development to production.
 
Bachelor's degree or higher in AI, Machine Learning, Computer Science, or related field.
 
3-5 years of experience in AI, Machine Learning, Data Science, or Application Development.
 
Experience with production AI deployments, MLOps, model monitoring, and cloud platforms.
GPT, Claude LLM, Stable Diffusion
 
LangChain
 
TensorFlow
 
PyTorch
 
Scikit-learn
 
RAG (Retrieval-Augmented Generation)
 
AI Agents &amp; Agentic Workflows
 
API Development
 
REST APIs
 
JSON
 
HTTP Requests
 
Authentication &amp; Tokens
 
Error Handling &amp; Debugging 
 
 
 
 
</div></div></div>The base compensation range for this role in the posted location is $90,000- $160,000
Capgemini provides compensation range information in accordance with applicable national, state, provincial, and local pay transparency laws. The base compensation range listed for this position reflects the minimum and maximum target compensation Capgemini, in good faith, believes it may pay for the role at the time of this posting. This range may be subject to change as permitted by law.
The actual compensation offered to any candidate may fall outside of the posted range and will be determined based on multiple factors legally permitted in the applicable jurisdiction.
These may include, but are not limited to: Geographic location, Education and qualifications, Certifications and licenses, Relevant experience and skills, Seniority and performance, Market and business consideration, Internal pay equity.
It is not typical for candidates to be hired at or near the top of the posted compensation range.
In addition to base salary, this role may be eligible for additional compensation such as variable incentives, bonuses, or commissions, depending on the position and applicable laws.
Capgemini offers a comprehensive, non-negotiable benefits package to all regular, full-time employees. In the U.S. and Canada, available benefits are determined by local policy and eligibility and may include:
<ul><li>Paid time off based on employee grade (A-F), defined by policy: Vacation: 12-25 days, depending on grade, Company paid holidays, Personal Days, Sick Leave</li>
</ul><ul><li>Medical, dental, and vision coverage (or provincial healthcare coordination in Canada)</li>
<li>Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)</li>
<li>Life and disability insurance</li>
<li>Employee assistance programs</li>
<li>Other benefits as provided by local policy and eligibility</li>
</ul>Important Notice: Compensation (including bonuses, commissions, or other forms of incentive pay) is not considered earned, vested, or payable until it becomes due under the terms of applicable plans or agreements and is subject to Capgemini&rsquo;s discretion, consistent with applicable laws. The Company reserves the right to amend or withdraw compensation programs at any time, within the limits of applicable legislation.
Disclaimers
Capgemini is an Equal Opportunity Employer encouraging inclusion in the workplace. Capgemini also participates in the Partnership Accreditation in Indigenous Relations (PAIR) program which supports meaningful engagement with Indigenous communities across Canada by promoting fairness, accessibility, inclusion and respect. We value the rich cultural heritage and contributions of Indigenous Peoples and actively work to create a welcoming and respectful environment. All qualified applicants will receive consideration for employment without regard to race, national origin, gender identity/expression, age, religion, disability, sexual orientation, genetics, veteran status, marital status or any other characteristic protected by law.
This is a general description of the Duties, Responsibilities and Qualifications required for this position. Physical, mental, sensory or environmental demands may be referenced in an attempt to communicate the manner in which this position traditionally is performed. Whenever necessary to provide individuals with disabilities an equal employment opportunity, Capgemini will consider reasonable accommodations that might involve varying job requirements and/or changing the way this job is performed, provided that such accommodation does not pose an undue hardship. Capgemini is committed to providing reasonable accommodation during our recruitment process. If you need assistance or accommodation, please reach out to your recruiting contact.
Please be aware that Capgemini may capture your image (video or screenshot) during the interview process and that image may be used for verification, including during the hiring and onboarding process.
Click the following link for more information on your rights as an Applicant in the United States. http://www.capgemini.com/resources/equal-employment-opportunity-is-the-law
Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end-to-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem.</div></div></div></div>

Data Scientist/ AI Engineer

Cantina Labs

<div><div><h2 class="jobSectionHeader">Location</h2>
Remote (U.S. or Europe)
</div><div><h2 class="jobSectionHeader">Employment Type</h2>
Full time
</div><div><h2 class="jobSectionHeader">Department</h2>
Research</div></div>
 <div><div>About Cantina:
Cantina Labs is a social AI company, developing a suite of advanced real-time models that push the boundaries of expression, personality, and realism. We bring characters to life, transforming how people tell stories, connect, and create. We build and power ecosystems. Cantina, our flagship social AI platform, is just the beginning.
If you're excited about the potential AI has to shape human creativity and social interactions, join us in building the future! 
<div></div>About the Role:
We are looking for an MLOps Engineer to build and scale the inference infrastructure for our generative audio models, including Text-to-Speech (TTS), voice conversion, and Automatic Speech Recognition (ASR). You will be responsible for designing and deploying high-performance systems that ensure low-latency, reliable, and scalable model serving for both streaming and batch inference. This role is central to bridging the gap between research and production, ensuring our audio models are optimized for performance and cost-efficiency as we scale.
What You&rsquo;ll Do:
<ul><li>Design and maintain inference infrastructure for generative audio model architectures.
</li><li>Implement and manage high-performance inference engines.
</li><li>Orchestrate service deployments using Kubernetes (K8S), implementing advanced autoscaling paradigms to handle varying traffic loads efficiently.
</li><li>Develop and automate robust CI/CD pipelines to streamline the testing and deployment of model artifacts and inference configurations.
</li><li>Monitor production systems, establishing observability practices to track latency, resource utilization, and overall model performance.
</li><li>Collaborate closely with research teams to optimize model serving paths and evaluate various inference strategies.
</li><li>Optimize inference performance for both streaming and batch applications.
</li></ul>What You&rsquo;ll Bring:
<ul><li>Deep understanding of modern audio model architectures (e.g., TTS, ASR) and their specific inference requirements.
</li><li>Strong hands-on experience with Kubernetes (K8S), container orchestration, and implementing autoscaling strategies for production workloads.
</li><li>Solid background in MLOps, including CI/CD automation and managing scalable cloud infrastructure.
</li><li>Proficiency in software engineering principles and experience with Python or Go for infrastructure tooling and backend services.
</li><li>Experience with GPU-accelerated inference and performance profiling techniques.
</li><li>Familiarity with high-performance inference engines (e.g., Triton Inference Server, vLLM-Omni) is a plus.
</li></ul>Compensation:
The anticipated annual base salary range for this role is between $125,000-$165,000 (&euro;110,000-&euro;145,000). When determining compensation, a number of factors will be considered, including skills, experience, job scope, location, and competitive compensation market data. 
<div></div>Benefits for U.S.-based roles:
<ul><li>Competitive salary and generous company equity
</li><li>Medical, dental, and vision insurance &ndash; 99.99% of premiums covered by Cantina
</li><li>42 days of paid time off, including:
<ul><li>15 PTO days
</li><li>10 sick days
</li><li>15 company holidays
</li><li>2 floating holidays
</li></ul></li><li>Generous parental leave &amp; fertility support
</li><li>401(k) retirement savings plan
</li><li>Lifestyle spending account &ndash; $500/month to use however you&rsquo;d like
</li><li>Complimentary lunch and snacks for in-office employees
</li><li>One Medical membership, and more!</li></ul></div></div>

Machine Learning Engineer, Ops

Occam Solutions

<div>Primary Responsibility 
The DevSecOps Engineer shall serve as the lead technical engineer responsible for designing, implementing, securing, automating, and sustaining the cloud infrastructure supporting the Office of Naval Research (ONR) Data &amp; Analytics environment. The DevSecOps Engineer shall lead the implementation and execution of the technical requirements identified in PWS Section 4.5 &ndash; Technical Infrastructure and Platform Support, including infrastructure automation, Continuous Integration/Continuous Deployment (CI/CD), cloud security, container orchestration, Infrastructure-as-Code (IaC), and operational sustainment of the FedRAMP High and DoD Impact Level 5 (IL5) cloud environment. 
The DevSecOps Engineer shall architect and maintain secure cloud infrastructure, automate deployment pipelines, support AI/ML platform integration, implement cloud-native security controls, monitor system health and security events, and ensure compliance with DoD cybersecurity requirements. This position will work closely with software developers, data engineers, cloud architects, cybersecurity personnel, and Government stakeholders to deliver resilient, scalable, and secure cloud services. 
Duties and Responsibilities

The DevSecOps Engineer shall:
<ul>
<li>Design, deploy, automate, and maintain secure cloud infrastructure supporting mission applications and analytics platforms.</li>
<li>Develop, implement, and manage automated CI/CD pipelines using tools such as Jenkins, GitLab CI/CD, or equivalent technologies.</li>
<li>Build and maintain Infrastructure-as-Code (IaC) solutions utilizing Terraform, Ansible, or similar automation frameworks.</li>
<li>Implement and manage containerized application environments using Docker, Kubernetes, or comparable orchestration platforms.</li>
<li>Configure, monitor, and optimize cloud-native security services including Web Application Firewalls (WAF), Intrusion Detection/Prevention Systems (IDS/IPS), endpoint protection, and Security Information and Event Management (SIEM) platforms.</li>
<li>Analyze security logs, audit events, vulnerability reports, and SIEM alerts to identify threats, recommend mitigation strategies, and support incident response activities.</li>
<li>Support continuous Authority to Operate (ATO) compliance through configuration management, security patching, vulnerability remediation, and security documentation.</li>
<li>Implement secure API management, identity and access management (IAM), secrets management, encryption, and least-privilege access controls.</li>
<li>Develop automated monitoring, logging, alerting, and operational dashboards to improve system reliability and performance.</li>
<li>Support deployment and operationalization of AI/ML platforms and services, including cloud-native machine learning environments.</li>
<li>Collaborate with software developers, data engineers, and cloud architects to integrate security throughout the software development lifecycle (SDLC).</li>
<li>Ensure cloud infrastructure complies with DoD cybersecurity policies, FedRAMP High requirements, DoD IL5 controls, and applicable security frameworks.</li>
<li>Support system scalability, resilience, disaster recovery, and operational continuity.</li>
<li>Document system architectures, deployment procedures, security configurations, and operational processes.</li></ul> 


Minimum Qualifications

The DevSecOps Engineer shall meet the requirements for the &quot;DevSecOps Engineer &ndash; Intermediate&quot; labor category as defined in the CHESS ITES-3S contract vehicle and possess the following additional qualifications:
<ul>
<li>Seven (7) years of combined experience in DevSecOps, DevOps, SecOps, or MLOps engineering and development.</li>
<li>Five (5) years of hands-on experience designing, securing, implementing, and maintaining cloud environments, preferably within AWS GovCloud, Azure Government, or similarly regulated cloud environments.</li>
<li>Demonstrated experience supporting cloud environments operating under FedRAMP High, DoD Impact Level 5 (IL5), or comparable federal security requirements.</li>
<li>Verifiable experience on at least two (2) projects designing, implementing, and managing Continuous Integration/Continuous Deployment (CI/CD) pipelines using Jenkins, GitLab CI/CD, Azure DevOps, GitHub Actions, or equivalent technologies.</li>
<li>Demonstrated expertise implementing and managing containerization and orchestration technologies, including Docker, Kubernetes, OpenShift, or similar platforms.</li>
<li>Hands-on experience implementing Infrastructure-as-Code (IaC) using Terraform, Ansible, CloudFormation, or comparable automation tools.</li>
<li>Experience configuring and administering cloud-native security capabilities including Web Application Firewalls (WAF), Intrusion Detection/Prevention Systems (IDS/IPS), Security Information and Event Management (SIEM), vulnerability management, and cloud security monitoring solutions.</li>
<li>Verifiable experience analyzing security logs, SIEM data, audit records, and threat intelligence to identify vulnerabilities, investigate security events, and implement corrective actions.</li>
<li>Experience implementing secure Identity and Access Management (IAM), encryption, secrets management, and Zero Trust security principles.</li>
<li>Experience supporting automated monitoring, logging, system performance optimization, and operational sustainment of enterprise cloud platforms.</li>
<li>Strong understanding of DevSecOps best practices, secure software development lifecycle (SSDLC), and cloud security architectures.</li> 
</ul>
Qualifications
<ul>
<li>Active Secret security clearance (required).</li>
<li>Current DoD 8140 (or legacy DoD 8570) cybersecurity certification applicable to privileged access functions (e.g., Security+, CySA+, CASP+, CISSP, CCSP).</li>
<li>AWS Certified DevOps Engineer &ndash; Professional, AWS Security Specialty, Azure DevOps Engineer Expert, Certified Kubernetes Administrator (CKA), or comparable cloud certifications.</li>
<li>Experience supporting AI/ML environments, including AWS SageMaker, Azure Machine Learning, or similar platforms.</li>
<li>Experience with streaming data technologies such as Apache Kafka, AWS Kinesis, or equivalent event-driven architectures.</li>
<li>Familiarity with NIST RMF, DISA STIGs, FedRAMP, Zero Trust Architecture, and DoD cybersecurity compliance requirements.</li></ul></div>

DevSecOps Engineer

Amgen

<div><div>United States - Remote
<div>JOB ID: R-250804 LOCATION: United States - Remote WORK LOCATION TYPE: Remote DATE POSTED: Jul. 29, 2026 CATEGORY: Information Systems SALARY RANGE: 129,654.75USD -175,415.25 USD
</div></div><div><div>Join Amgen&rsquo;s Mission of Serving Patients
At Amgen, if you feel like you&rsquo;re part of something bigger, it&rsquo;s because you are. Our shared mission&mdash;to serve patients living with serious illnesses&mdash;drives all that we do.
Since 1980, we&rsquo;ve helped pioneer the world of biotech in our fight against the world&rsquo;s toughest diseases. With our focus on four therapeutic areas &ndash;Oncology, Inflammation, General Medicine, and Rare Disease&ndash; we reach millions of patients each year. Amgen is advancing a broad and deep pipeline of medicines to treat cancer, heart disease, inflammatory conditions, rare diseases, and obesity and obesity-related conditions. As a member of the Amgen team, you&rsquo;ll help make a lasting impact on the lives of patients as we research, manufacture, and deliver innovative medicines to help people live longer, fuller happier lives.
Our award-winning culture is collaborative, innovative, and science based. If you have a passion for challenges and the opportunities that lay within them, you&rsquo;ll thrive as part of the Amgen team. Join us and transform the lives of patients while transforming your career.
Machine Learning Engineer
What you will do
Let&rsquo;s do this. Let&rsquo;s change the world. In this vital role, you will contribute to the development of scalable machine learning platforms and workflows that enable scientists and researchers across Amgen to build, deploy, and manage AI/ML models. You will work closely with experienced engineers, data scientists, and domain experts to productionize machine learning solutions primarily in support of drug discovery and development.
This is an ideal role for engineers early in their ML engineering careers who want to grow their expertise in MLOps, cloud platforms, and applied AI in life sciences.
This role will be eligible for remote in the US.
Roles &amp; Responsibilities:<ul><li>
Deliver AI and ML-enabled applications, with deployed models ranging from classical ML models to natural language processing, protein language models, and large language models.</li><li>
Contribute to the development and maintenance of ML platform capabilities, including:<ul><li>
Data pipelines and feature engineering workflows</li><li>
Model training, evaluation, and deployment pipelines</li><li>
Experiment tracking and model registry systems</li><li>
Model performance evaluations and monitoring</li></ul></li><li>
Partner in implementation of AI and ML Ops best practices, including CI/CD, infra as code, monitoring, traceability and reproducibility.</li><li>
Collaborate with cross-functional teams to transition standards and outcomes from experimentation to production-grade enterprise solutions.</li><li>
Build and maintain scalable and efficient, productionized MLOps solutions on cloud platforms.</li><li>
Develop and deliver training content and knowledge articles to educate resident scientists on model lifecycle management best practices.</li></ul>
What we expect of you
We are all different, yet we all use our unique contributions to serve patients. The professional we seek is an individual with these qualifications.
Basic Qualifications:
Master&rsquo;s degree
OR
Bachelor&rsquo;s degree and 2 years of experience in Computer Science, IT, or engineering field
OR
Associate&rsquo;s degree and 6 years in Computer Science, IT, or engineering field
OR
High school diploma / GED and 8 years in Computer Science, IT, or engineering field
Required Skills:<ul><li>
Software programming (Python, version control, test-driven development).</li><li>
Familiarity with cloud technologies (AWS preferred, Databricks).</li><li>
Hands-on experience with AI/ML model training and serving.</li></ul>
Preferred Qualifications:<ul><li>
Experience with deployment and maintenance of AI/ML models in production.</li><li>
Substantial experience with cloud technologies and platforms, such as Databricks, AWS (preferred), or Azure.</li><li>
Experience with AI and MLOps frameworks and tools (MLflow, Kubeflow, Weights &amp; Biases, Terraform, etc.).</li><li>
Experience with containerization (Docker, Kubernetes).</li><li>
Familiarity with distributed data processing (e.g., Spark).</li><li>
Familiarity with data science libraries (scikit-learn, TensorFlow, PyTorch, LangChain).</li></ul>
Soft Skills:<ul><li>
Excellent critical-thinking and problem-solving skills.</li><li>
Strong communication and collaboration skills.</li><li>
Demonstrated presentation skills.</li><li>
Self-motivated, with independent ownership and leadership skills.</li></ul>
What you can expect of us
As we work to develop treatments that take care of others, we also work to care for your professional and personal growth and well-being. From our competitive benefits to our collaborative culture, we&rsquo;ll support your journey every step of the way.
The expected annual salary range for this role in the U.S. (excluding Puerto Rico) is posted. Actual salary will vary based on several factors including but not limited to, relevant skills, experience, and qualifications.
In addition to the base salary, Amgen offers a Total Rewards Plan, based on eligibility, comprising of health and welfare plans for staff and eligible dependents, financial plans with opportunities to save towards retirement or other goals, work/life balance, and career development opportunities that may include:<ul><li>
A comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions, group medical, dental and vision coverage, life and disability insurance, and flexible spending accounts</li><li>
A discretionary annual bonus program, or for field sales representatives, a sales-based incentive plan</li><li>
Stock-based long-term incentives</li><li>
Award-winning time-off plans</li><li>
Flexible work models where possible. Refer to the Work Location Type in the job posting to see if this applies</li></ul>
 and make a lasting impact with the Amgen team.
careers.amgen.com
In any materials you submit, you may redact or remove age-identifying information such as age, date of birth, or dates of school attendance or graduation. You will not be penalized for redacting or removing this information.
Application deadline
Amgen does not have an application deadline for this position; we will continue accepting applications until we receive a sufficient number or select a candidate for the position.
Sponsorship
Sponsorship for this role is not guaranteed.
As an organization dedicated to improving the quality of life for people around the world, Amgen fosters an inclusive environment of diverse, ethical, committed and highly accomplished people who respect each other and live the Amgen values to continue advancing science to serve patients. Together, we compete in the fight against serious disease.
Amgen is an Equal Opportunity employer and will consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or any other basis protected by applicable law.
We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.</div></div></div>

Machine Learning Engineer

Veeam Software

<div><div><div>Veeam is the Data and AI Trust Company, specializing in helping organizations ensure their data and AI are fully understood, secured, and resilient to enable the acceleration of safe AI at scale. As the market leader in both data resilience and data security posture management, Veeam is built for the convergence of identity, data, security, and AI risk. Headquartered in Seattle with offices in more than 30 countries, Veeam protects over 550,000 customers worldwide, who trust Veeam to keep their businesses running. Join us as we go fearlessly forward together, growing, learning, and making a real impact for some of the world's biggest brands.
</div></div>
About the role:

Senior Domain Engineering Specialist represent Veeam's deepest technical bench and operate as a specialized overlay function supporting eligible accounts in close partnership with Customer Success Engineers (CSEs), Customer Success Specialist (CSSs), and Sales teams. This role is designed to be pulled into customer engagements when advanced product expertise or domain depth is required to accelerate outcomes, derisk adoption, or support complex expansion opportunities.

The Domain Engineering team is intentionally multidimensional and will include:
<ul>
<li>Product focused specialists across VDC and the broader Veeam Data Platform (VDP).</li>
</ul><ul><li>Domain experts spanning Security, Cloud, and AI.</li>
</ul>
Success Engineers blend deep technical credibility with strong customer facing and consultative skills. They engage directly with senior customer stakeholders&mdash;including CIOs and CISOs&mdash;to assess resilience posture, guide solution design, and influence long term roadmap strategy. By amplifying Veeam's technical authority, Success Engineers help ensure customers realize maximum value while enabling Sales and CSE teams to operate with greater focus and efficiency.

What You'll Do
<ul>
<li>Serve as a technical overlay to CSEs and CSSs, providing specialized expertise when advanced product or domain depth is required.</li>
</ul><ul><li>Lead solution design reviews, customer readiness assessments, and risk or maturity modeling engagements across the Veeam Data Platform.</li>
</ul><ul><li>Engage directly with CIO and CISO stakeholders on resilience posture, risk exposure, and longterm data protection and modernization strategies.</li>
</ul><ul><li>Support security, cloud, and AI expansion motions in partnership with Sales and CSE teams, helping shape technical strategy and customer narratives.</li>
</ul><ul><li>Monitor telemetry, inspection signals, and maturity indicators (e.g., DRMM where applicable) to identify risk, optimization opportunities, and expansion signals.</li>
</ul><ul><li>Validate architectures and designs to derisk adoption and accelerate time to value.</li>
</ul><ul><li>Align tightly with Sales Engineering teams on required deliverables and engagement strategy, enabling Sales to remain focused on presales motions while Success Engineers support post sale depth and continuity.</li>
</ul><ul><li>Operate as a pooled specialist resource across Enterprise and Commercial Named accounts (generally $100K+ ARR), with coverage models aligned to demand and specialization.</li>
</ul><ul><li>Amplify Veeam's technical authority in customer conversations by translating complex technical concepts into clear, outcome oriented guidance.</li>
</ul>
Note: Success Engineers are not the primary owners of onboarding or renewals but provide targeted support where advanced technical involvement materially improves outcomes.

What You'll Bring
<ul>
<li>5+ years of experience in advanced technical or architecture roles (e.g., Security Engineer/Architect, Cloud Solution Architect, Platform Engineer, MLOps / ML Engineer).</li>
</ul><ul><li>Strong depth in at least one product area (VDC or VDP) or one domain area (Security, Cloud, AI), with the ability to collaborate across specialties.</li>
</ul><ul><li>Bachelor's degree in Computer Science, Electrical Engineering, or a related technical field; advanced degree preferred.</li>
</ul><ul><li>Relevant industry certifications (e.g., CompTIA Security+, CISSP, cloud or platform certifications); VMCE preferred or willingness to complete posthire.</li>
</ul><ul><li>Demonstrated ability to engage senior technical and executive stakeholders (CIO/CISO) on risk, resilience, and strategic roadmaps.</li>
</ul><ul><li>Handson experience with solution design, proofs of concept, telemetry analysis, and maturity or risk modeling frameworks.</li>
</ul><ul><li>Strong communication, stakeholder management, and cross functional collaboration skills.</li>
</ul><ul><li>Comfort operating in an overlay model, balancing multiple accounts and priorities with impact.</li>
</ul>
#LI-TN1 
#LI-REMOTE
<div><div>
Veeam Software is an equal opportunity employer and does not tolerate discrimination in any form on the basis of race, color, religion, gender, age, national origin, citizenship, disability, veteran status or any other classification protected by federal, state or local law. All your information will be kept confidential.

Personal data collected during the recruitment process will be processed in accordance with our Recruiting Privacy Notice, which explains how your information is collected, used, and handled in connection with hiring activities. By applying for this position, you consent to this processing.

By submitting your application, you confirm that the information provided, including any supporting documents, is complete and accurate to the best of your knowledge. Any misrepresentation, omission, or falsification may result in disqualification from consideration or, if discovered after employment begins, termination of employment.</div></div></div>

Senior Domain Engineering Specialist- VDP & Cloud

HCLTech

Frisco, Texas
 Job Summary
 
The Generative AI Engineer will be responsible for designing, developing, and deploying AI-driven solutions leveraging large language models (LLMs), diffusion models, and other generative frameworks. The role involves collaborating closely with data scientists, software engineers, and business teams to create intelligent, context-aware applications that enhance automation, creativity, and decision-making across the enterprise.
 
Key Responsibilities
 
Design, fine-tune, and deploy large language models (LLMs) and other generative models for specific business use cases.
 <ul>
<li>Build and optimize prompt engineering and model interaction strategies for improved output accuracy and relevance.</li>
<li>Develop APIs, pipelines, and integrations to embed generative AI capabilities into products and workflows.</li>
<li>Collaborate with data engineers to ensure high-quality, domain-relevant datasets for model training and evaluation.</li>
<li>Implement responsible AI principles, including bias mitigation, explainability, and data governance.</li>
<li>Monitor model performance and continuously refine models through feedback loops and retraining.</li>
<li>Stay up to date with emerging trends in AI research, frameworks (e.g., LangChain, Hugging Face, OpenAI API), and open-source tools.</li>
<li>Contribute to internal knowledge sharing and best practices for scalable AI deployment.</li>
</ul>
Skill Requirements
 
o Proficiency in Python and frameworks such as PyTorch or TensorFlow.
 
o Experience in SQL, Machine Learning, Big Dat and other Databases.
 
o Experience with LLM APIs (OpenAI, Anthropic, Gemini, etc.) and vector databases (Pinecone, FAISS, Weaviate).
 o Familiarity with prompt engineering, model fine-tuning, and reinforcement learning from human feedback (RLHF).
 o Strong understanding of NLP, deep learning, and data pipeline design.
 o Exposure to cloud platforms (AWS, Azure, GCP) and MLOps tools for model deployment.
 o Strong problem-solving and analytical thinking.
 
Other Requirements
 
Certifications in GenAI or Agentic AI
 
Maximum Salary (US):Minimum Salary (US): 
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Platform Engineer I

BV Teck

<div>Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. 
 
This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential. 
 
Job Title: Foundation Model Engineer 
Location: 100% Remote (U.S.) 
Position Type: Full-time, Direct W2 
Salary Range: $200,000&ndash;$230,000 Annually 
Experience Required: 6+ years 
 
Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position. 
 
Job Summary: 
We are looking for an Foundation Model Engineer to design, execute, and operationalize fine-tuning workflows for large language models across supervised, preference-based, and reinforcement learning approaches. The role requires deep practical experience with modern training stacks, careful dataset construction, rigorous evaluation methodology, and the engineering discipline to operate complex training pipelines reliably. The ideal candidate combines strong ML intuition with production-grade engineering practices, and is comfortable navigating the trade-offs between data quality, compute budget, evaluation rigor, and shipping velocity. In this role you will work closely with cross-functional partners &mdash; product, design, engineering, operations, and business stakeholders &mdash; to translate ambiguous requirements into well-engineered solutions, and will be expected to raise the bar through code review, design review, and mentorship of more junior engineers. The successful candidate brings strong engineering discipline, a clear communication style, and a track record of shipping meaningful work that holds up well in production. 
<h2 class="jobSectionHeader">
Key Responsibilities</h2><ul><li>
Develop, fine-tune, and optimize Large Language Models (LLMs) for enterprise AI applications.</li><li>
Design and implement end-to-end model training and fine-tuning pipelines using PyTorch, Hugging Face Transformers, and related frameworks.</li><li>
Prepare, clean, and curate training datasets for supervised fine-tuning and instruction tuning.</li><li>
Implement parameter-efficient fine-tuning techniques such as LoRA, QLoRA, and PEFT to improve training efficiency.</li><li>
Evaluate model performance using standard NLP benchmarks, automated metrics, and task-specific evaluations.</li><li>
Collaborate with data scientists, software engineers, and product teams to integrate LLM solutions into production applications.</li><li>
Optimize model inference performance, latency, and resource utilization for scalable deployment.</li><li>
Develop data preprocessing, feature engineering, and automation scripts using Python.</li><li>
Deploy and monitor machine learning models on cloud platforms using MLOps best practices.</li><li>
Troubleshoot model training issues, optimize hyperparameters, and improve overall model accuracy.</li><li>
Document technical designs, experiments, and implementation details for knowledge sharing.</li><li>
Stay current with advancements in Generative AI, transformer architectures, and open-source LLM technologies.</li></ul><h2 class="jobSectionHeader">
Required Qualifications</h2><ul><li>
Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field. Equivalent industry experience will also be considered.</li><li>
6+ years of experience in Machine Learning or AI engineering, including at least 2 years working with Large Language Models (LLMs) or Generative AI.</li><li>
Strong programming skills in Python.</li><li>
Hands-on experience with PyTorch and Hugging Face Transformers.</li><li>
Experience fine-tuning open-source LLMs such as Llama, Mistral, Falcon, Gemma, or similar transformer-based models.</li><li>
Knowledge of parameter-efficient fine-tuning techniques including LoRA, QLoRA, or PEFT.</li><li>
Experience building data preprocessing and model training pipelines.</li><li>
Familiarity with vector databases, embeddings, and Retrieval-Augmented Generation (RAG) concepts.</li><li>
Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.</li><li>
Working knowledge of Docker, Kubernetes, Git, and CI/CD practices.</li><li>
Strong analytical, problem-solving, and debugging skills.</li><li>
Excellent communication and collaboration skills.</li></ul><h2 class="jobSectionHeader">
Preferred Qualifications</h2><ul><li>
Experience with LangChain, LlamaIndex, DSPy, or similar LLM orchestration frameworks.</li><li>
Familiarity with distributed model training technologies such as DeepSpeed or FSDP.</li><li>
Experience deploying LLMs using vLLM, TensorRT-LLM, or ONNX Runtime.</li><li>
Knowledge of ML lifecycle and MLOps tools such as MLflow, Kubeflow, or Weights &amp; Biases.</li><li>
Experience building enterprise AI chatbots, copilots, document intelligence, or RAG-based applications.</li><li>
Exposure to prompt engineering, AI evaluation frameworks, and LLM safety best practices.</li><li>
Contributions to open-source AI or machine learning projects are a plus.</li><li>
Experience working in Agile software development environments.</li></ul> 

How to Apply 
 
Would you like to know more about this opportunity? For immediate consideration, please send your resume to Harry@bvteck.com or contact us at (908)676-4399. Learn more about Bright Vision Technologies at www.bvteck.com. 
 
Bright Vision Technologies is an Equal Opportunity Employer
Equal Employment Opportunity (EEO) Statement
Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall.
BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.
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Foundation Model Engineer

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