mlops engineer

Uth Business Solutions Pvt. Ltd.

<p><b>Job Title:</b> MLOps Engineer</p><p><b>Company:</b> DwarfEye Labs<br/><b>Location:</b> Chennai, Tamil Nadu, India<br/><b>Employment Type:</b> Full-Time (On-site/Hybrid &mdash; specify as per your policy)<br/><b>Experience:</b> Minimum 6 months-1+ years in MLOps / Machine Learning Engineering</p><p><b>About DwarfEye Labs</b></p><p>DwarfEye Labs is a frontier AI technology company dedicated to building <b>next-generation video analytics and computer vision solutions</b> that transform how machines perceive and interpret visual data. Our products power intelligent insights across surveillance, safety, automation, and industrial analytics.</p><p>We are committed to innovation, excellence, and scalable AI systems that make an impact.</p><p><b>Role Overview</b></p><p>We are seeking a <b>skilled and motivated MLOps Engineer</b> with <i>at least 6 months-1+ year of hands-on experience</i> in building, deploying, and maintaining production-grade machine learning pipelines. The ideal candidate will have a strong blend of software engineering, DevOps, and ML systems experience &mdash; and be passionate about operationalizing ML models at scale.</p><p><b>Key Responsibilities</b></p><ul><li>Design, implement, and manage <i>end-to-end MLOps pipelines</i> &mdash; from data ingestion to production deployment and monitoring.</li><li>Collaborate with Data Scientists and AI Engineers to deploy machine learning models reliably in production.</li><li>Automate CI/CD workflows for ML workflows using tools like <b>Airflow, Jenkins, Kubeflow, MLFlow, Seldon Core, TFX</b>, etc.</li><li>Manage infrastructure using <b>containers, orchestration (Docker, Kubernetes)</b>, and cloud services (AWS / GCP / Azure).</li><li>Monitor model performance and system health; implement alerting &amp; logging best practices.</li><li>Drive optimization and operational excellence in ML delivery processes.</li></ul><p><b>Skills &amp; Qualifications</b></p><p><b>Must-have:</b></p><ul><li>Bachelor&rsquo;s degree in Computer Science, Engineering, or related field.</li><li>6 months-1+ years of professional experience in MLOps or Machine Learning Engineering.</li><li>Proficiency with Python and ML ecosystem tools (TensorFlow / PyTorch / Scikit-learn).</li><li>Experience with <b>CI/CD automation, containerization (Docker), and orchestration (Kubernetes)</b>.</li><li>Exposure to cloud platforms (AWS, Azure, or GCP).</li><li>Strong understanding of ML lifecycle, versioning, and reproducibility.</li></ul><p><b>Preferred:</b></p><ul><li>Familiarity with workflow schedulers (Airflow / Argo).</li><li>Experience with feature stores, model registries (MLflow), and monitoring tools.</li><li>Comfortable working in agile teams with cross-functional collaboration.</li></ul><p><b>What We Offer</b></p><ul><li>Opportunity to work on cutting-edge AI &amp; Computer Vision products.</li><li>Collaborative and innovative work culture.</li><li>Learning &amp; growth opportunities in AI/ML technology stack.</li><li>Competitive benefits and compensation package.</li></ul><p><b>Salary Range</b></p><ul><li><b>6 months&ndash;1 Year Experience:</b> ₹3,60,000 &ndash; ₹4,80,000 per annum <i>(Final offer will depend on skills, experience, expertise with cloud/MLOps tools and interview performance.)</i></li></ul><p><b>How to Apply</b></p><p>Email your resume to: <b>karthik@dwarfeye.ai</b><br/><b>Subject line</b>: <i>Application for MLOps Engineer &ndash; [Your Name]</i></p><p>Job Type: Full-time</p><p>Pay: ₹360,000.00 - ₹480,000.00 per year</p><p>Work Location: In person</p>

MLOps Engineer

Qualys

<div><div>Come work at a place where innovation and teamwork come together to support the most exciting missions in the world!</div><div></div><div>
Are you passionate about building and deploying machine learning models in production? Do you thrive in a</div><div>
collaborative environment and enjoy tackling complex technical challenges? If so, then we want to hear from you!</div><div>
We are seeking a talented and experienced Machine Learning Operations (MLOps) Engineer to join our team. As</div><div>
an MLOps Engineer, you will play a critical role in deploying, maintaining, and optimizing machine learning</div><div>
models. Also, you will be responsible for the entire lifecycle of our models, from development to production. You</div><div>
will work closely with data scientists, software engineers, and other stakeholders to ensure the seamless</div><div>
integration of machine learning solutions into our products and services.</div><div></div><div><br>
Responsibilities:</div><ul><li>Design and implement CI/CD pipelines for machine learning models using tools like GitLab CI, GitHub</li></ul><div>
Actions, CircleCI, or Airflow.</div><ul><li>Deploy machine learning models into production environments, establish monitoring systems to track</li></ul><div>
model performance and data drift, and implement automated alerts for potential issues.</div><ul><li>Manage and optimize the infrastructure required for running machine learning models, including cloud</li></ul><div>
resources, containers, and orchestration tools. Containerize, version control, and deploy machine</div><div>
learning models.</div><ul><li>Implement version control systems for machine learning models and collaborate with data scientists</li></ul><div>
and engineers to streamline the model development and deployment process.</div><ul><li>Develop automation scripts and tools to streamline the deployment and scaling of machine learning</li></ul><div>
solutions, ensuring high availability and reliability.</div><ul><li>Implement security best practices for machine learning systems, ensure compliance with data privacy</li></ul><div>
regulations, and participate in security audits and assessments.</div><ul><li>Identify opportunities to optimize the performance of machine learning models, including tuning</li></ul><div>
hyperparameters, improving inference speed, and reducing resource consumption.</div><ul><li>Stay up to date with the latest advancements in MLOps and related technologies.</li><li>Document processes and best practices for MLOps within the team.</li></ul><div></div><div><br>
Qualifications:</div><ul><li>Bachelor&rsquo;s or master&rsquo;s degree in computer science, Engineering, or a related field (or equivalent</li></ul><div>
experience).</div><ul><li>Proven experience in machine learning operations, DevOps, or software engineering roles.</li><li>Strong understanding of CI/CD principles and experience with CI/CD tools.</li><li>Strong programming skills in languages such as Python, Java, or Go.</li><li>Proficiency in containerization technologies (Docker, Kubernetes) and orchestration tools.</li><li>Experience with MLOps tools (MLflow, Kubeflow, Metaflow) is a plus.</li><li>Familiarity with machine learning frameworks (TensorFlow, PyTorch, scikit-learn) is a plus.</li><li>Excellent communication and collaboration skills.</li><li>Ability to work independently and as part of a cross-functional team.</li><li>Problem-solving skills and a passion for learning new technologies.</li></ul></div>

Capgemini

<div><div><div><p>Bangalore, Hyderabad</p><div>
MLOps Engineer</div><p>
</p></div></div><div></div><div><div><div><div><div><p>
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.
</p><div><div><div><div><b>Your Role</b></div></div><div><ul><li>
Design, implement, and maintain end-to-end ML pipelines for model training, evaluation, and deployment</li></ul><ul><li>
Collaborate with data scientists and software engineers to operationalize ML models, serving frameworks (TensorFlow Serving, TorchServe) and experience with MLOps tools</li></ul><ul><li>
Develop and maintain CI/CD pipelines for ML workflows</li></ul><ul><li>
Implement monitoring and logging solutions for ML models, experience with ML model serving frameworks (TensorFlow Serving, TorchServe)</li></ul><ul><li>
Optimize ML infrastructure for performance, scalability, and cost-efficiency</li></ul></div></div><div><div><div><b>
Your Profile</b></div></div><div><ul><li>
Strong programming skills in Python (5+ years), with experience in ML frameworks; understanding of ML-specific testing and validation techniques</li><li>
Expertise in containerization technologies (Docker) and orchestration platforms (Kubernetes), Knowledge of data versioning and model versioning techniques</li><li>
Proficiency in cloud platform (AWS) and their ML-specific services with atleast 2-3 years of experience.</li><li>
Strong understanding of DevOps practices and tools (GitLab, Artifactory, Gitflow etc.)</li><li>
Experience with monitoring and observability tools (Prometheus, Grafana, ELK stack) and knowledge of distributed training techniques</li></ul></div></div><div><div><div><b>
What you&rsquo;ll love about working here</b></div></div><div><ul><li>
We recognise the significance of flexible work arrangements to provide support in hybrid mode, you will get an environment to maintain healthy work life balance</li><li>
Our focus will be your career growth &amp; professional development to support you in exploring the world of opportunities.</li><li>
Equip yourself with valuable certifications &amp; training programmes in the latest technologies such as MLOps, Machine Learning</li></ul><p>
</p></div></div></div><p></p><p>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.</p></div></div><div><div><div>
<div><div>Ref. code</div><div>
326073-en_GB</div></div><div><div>
Posted on</div><div>
10 Sep 2025</div></div><div><div>
Experience level</div><div>
Experienced Professionals</div></div><div><div>
Contract type</div><div>
Permanent</div></div><div><div>
Location</div><div>
Bangalore, Hyderabad</div></div><div><div>
Business unit</div><div>
I and D Global Business Line</div></div><div><div>
Brand</div><div>
Capgemini</div></div></div></div></div></div></div></div></div><p></p>

WITS INNOVATION LAB

<p><b>Key Responsibilities:-</b></p><ul><li>Design, develop, and implement MLOps pipelines for the continuous deployment an integration of machine learning models</li><li>Collaborate with data scientists and engineers to understand model requirements and optimize deployment processes</li><li>Automate the training, testing and deployment processes for machine learning models</li><li>Continuously monitor and maintain models in production, ensuring optimal performance, accuracy and reliability</li><li>Implement best practices for version control, model reproducibility and governance</li><li>Optimize machine learning pipelines for scalability, efficiency and cost-effectiveness</li><li>Troubleshoot and resolve issues related to model deployment and performance</li><li>Ensure compliance with security and data privacy standards in all MLOps activities</li><li>Keep up to date with the latest MLOps tools, technologies and trends</li><li>Provide support and guidance to other team members on MLOps practices</li></ul><p><b>Required skills and experience</b></p><ul><li>3-10 years of experience in MLOps, DevOps or a related field</li><li>Bachelor&rsquo;s degree in computer science, Data Science or a related field</li><li>Strong understanding of machine learning principles and model lifecycle management</li><li>Experience in Jenkins pipeline development</li><li>Experience in automation scripting</li></ul><p>Job Type: Full-time</p><p>Pay: Up to &#8377;1,700,000.00 per year</p><p>Experience:</p><ul><li>MLOps: 3 years (Required)</li></ul><p>Work Location: In person</p>

<div><div>Responsibilities</div><div>
1. Design, develop, and implement MLOps pipelines for the continuous deployment and</div><div>
integration of machine learning models</div><div>
2. Collaborate with data scientists and engineers to understand model requirements and</div><div>
optimize deployment processes</div><div>
3. 4. Automate the training, testing and deployment processes for machine learning models</div><div>
Continuously monitor and maintain models in production, ensuring optimal</div><div>
performance, accuracy and reliability</div><div>
5. 6. 7. 8. 9. Implement best practices for version control, model reproducibility and governance</div><div>
Optimize machine learning pipelines for scalability, efficiency and cost-effectiveness</div><div>
Troubleshoot and resolve issues related to model deployment and performance</div><div>
Ensure compliance with security and data privacy standards in all MLOps activities</div><div>
Keep up to date with the latest MLOps tools, technologies and trends</div><div>
10. Provide support and guidance to other team members on MLOps practices</div><div></div><div><br>
Required skills and experience</div><ul><li>3-10 years of experience in MLOps, DevOps or a related field</li><li>Bachelor&rsquo;s degree in computer science, Data Science or a related field</li><li>Strong understanding of machine learning principles and model lifecycle management</li><li>Experience in Jenkins pipeline development</li><li>Experience in automation scripting</li></ul></div>

Wits Innovation Lab

<p><b>Responsibilities:-</b></p><ul><li>Design, develop, and implement MLOps pipelines for the continuous deployment and integration of machine learning models</li><li>Collaborate with data scientists and engineers to understand model requirements and optimize deployment processes</li><li>Automate the training, testing and deployment processes for machine learning models</li><li>Continuously monitor and maintain models in production, ensuring optimal performance, accuracy and reliability</li><li>Implement best practices for version control, model reproducibility and governance</li><li>Optimize machine learning pipelines for scalability, efficiency and cost-effectiveness</li><li>Troubleshoot and resolve issues related to model deployment and performance</li><li>Ensure compliance with security and data privacy standards in all MLOps activities</li><li>Keep up to date with the latest MLOps tools, technologies and trends</li><li>Provide support and guidance to other team members on MLOps practices</li></ul><p><b>Required skills and experience:- </b></p><ul><li>3-10 years of experience in MLOps, DevOps or a related field</li><li>Bachelor&rsquo;s degree in computer science, Data Science or a related field</li><li>Strong understanding of machine learning principles and model lifecycle management</li><li>Experience in Jenkins pipeline development</li><li>Experience in automation scripting</li></ul><p>Job Type: Full-time</p><p>Pay: ₹1,200,000.00 - ₹2,200,000.00 per year</p><p>Benefits:</p><ul><li>Health insurance</li><li>Paid sick time</li><li>Provident Fund</li></ul><p>Work Location: In person</p>

Infinite Technology Solutions

<p><b>Key Responsibilities1. ML Pipeline Development</b></p><ul><li>Build and automate end-to-end ML pipelines (data ingestion &rarr; preprocessing &rarr; training &rarr; deployment).</li><li>Develop reusable workflows using tools like <b>Kubeflow, Airflow, MLflow, DVC, Metaflow</b>.</li><li>Implement scalable model training on cloud GPUs/CPUs.</li></ul><p><b>2. Model Deployment &amp; Serving</b></p><ul><li>Deploy ML &amp; LLM models to production using:</li><li>Docker, Kubernetes</li><li>AWS Sagemaker, GCP Vertex AI, Azure ML</li><li>ONNX, Triton Inference Server, Ray Serve</li><li>Implement high-availability model endpoints with autoscaling.</li></ul><p><b>3. CI/CD for Machine Learning</b></p><ul><li>Build automated CI/CD pipelines for ML workflows using GitHub Actions, GitLab CI, or Jenkins.</li><li>Integrate continuous training (CT) and continuous deployment (CD) for models.</li><li>Version management for code, data, and models.</li></ul><p><b>4. Monitoring &amp; Observability</b></p><ul><li>Monitor model performance (accuracy drift, data drift, concept drift).</li><li>Set up logs, metrics, alerts using Prometheus, Grafana, ELK, or similar tools.</li><li>Maintain dashboards for model health and prediction quality.</li></ul><p><b>5. Data Engineering &amp; Governance</b></p><ul><li>Collaborate with data engineers to maintain robust data pipelines.</li><li>Ensure data quality, lineage, versioning, governance, and compliance.</li><li>Manage feature stores (Feast, Tecton, Vertex Feature Store).</li></ul><p><b>6. Infrastructure &amp; Cloud Management</b></p><ul><li>Build and maintain cloud infrastructure for ML workloads.</li><li>Optimize model serving costs and performance.</li><li>Manage containerized environments using Docker &amp; Kubernetes.</li></ul><p><b>7. Collaboration &amp; Best Practices</b></p><ul><li>Work closely with data scientists, engineers, and product teams.</li><li>Translate ML requirements into scalable infrastructure solutions.</li><li>Establish and enforce MLOps best practices across teams.</li></ul><p><b>Required Skills &amp; QualificationsTechnical Skills</b></p><ul><li>Strong experience in <b>Python</b>, ML libraries, and API development.</li><li>Expertise with ML platforms: <b>Airflow, MLflow, DVC, Kubeflow, Ray, Tecton</b>.</li><li>Hands-on experience with <b>Docker, Kubernetes, Terraform/Ansible</b>.</li><li>Strong command of <b>AWS/GCP/Azure</b> cloud ML services.</li><li>Knowledge of CI/CD pipelines.</li><li>Experience with monitoring tools (Prometheus, Grafana, ELK).</li><li>Familiarity with data versioning, ETL pipelines, and feature stores.</li><li>Understanding of LLM Ops for deploying large language models.</li></ul><p><b>Soft Skills</b></p><ul><li>Strong communication and documentation abilities.</li><li>Analytical, problem-solving mindset.</li><li>Ability to collaborate with cross-functional teams.</li><li>Independent ownership of ML deployments and infra decisions.</li></ul><p><b>Preferred Qualifications</b></p><ul><li>Bachelor&rsquo;s or Master&rsquo;s in Computer Science, AI/ML, Data Science, or related fields.</li><li>Certifications: AWS ML Specialty, Google ML Engineer, Kubernetes CKA/CKAD.</li><li>Experience with LLM fine-tuning and vector databases.</li><li>Prior production experience with NLP/CV/LLM models.</li></ul><p><b>Key KPIs</b></p><ul><li>Uptime and availability of ML model APIs.</li><li>Deployment frequency and automation success rate.</li><li>Reduction in training/serving costs.</li><li>Monitoring and alert efficiency (drift detection, performance stability).</li><li>Faster lead time from model development to production.</li></ul><p><b>Why Join Us</b></p><ul><li>Work with cutting-edge AI/ML technologies.</li><li>Opportunity to build a world-class MLOps infrastructure.</li><li>Fast-paced environment with strong career growth.</li><li>Work on impactful AI projects across industries.</li></ul><p>Job Types: Full-time, Part-time, Freelance</p><p>Pay: &#8377;271,719.81 - &#8377;1,166,266.45 per year</p><p>Work Location: In person</p>

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