Senior Machine Learning Engineer (Production & MLOps Focus)

Senior DevOps Engineer

Principal Applied Data Scientist

Senior Devops Engineer

Responsibilities: <ul><li>Design, deploy, and maintain production-ready ML models and pipelines for real-world applications.&nbsp;</li><li>Build and scale ML pipelines using Vertex AI Pipelines, Kubeflow, Airflow, and manage infra-as-code with Terraform/Helm.&nbsp;</li><li>Implement automated retraining, drift detection, and re-deployment of ML models.&nbsp;</li><li>Develop CI/CD workflows (GitHub Actions, GitLab CI, Jenkins) tailored for ML.&nbsp;</li><li>Implement model monitoring, observability, and alerting across accuracy, latency, and cost.&nbsp;</li><li>Integrate and manage feature stores, knowledge graphs, and vector databases for advanced ML/RAG use cases.&nbsp;</li><li>Ensure pipelines are secure, compliant, and cost-optimized.&nbsp;&nbsp;</li><li>Drive adoption of MLOps best practices: develop and maintain workflows to ensure reproducibility, versioning, lineage tracking, governance .&nbsp;</li><li>Mentor junior engineers and contribute to long-term ML platform architecture design and technical roadmap.&nbsp;</li><li>Stay current with the latest ML research and apply new tools pragmatically to production systems.&nbsp;</li><li>Collaborate with product managers, DS, and engineers to translate business problems into reliable ML systems.&nbsp;</li></ul> Requirements: <ul><li>Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or related field (PhD is a plus).&nbsp;</li><li>5+ years of experience in machine learning engineering, MLOps, or large-scale AI/DS systems.&nbsp;</li><li>Strong foundations in data structures, algorithms, and distributed systems.&nbsp;</li><li>Proficient in Python (scikit-learn, PyTorch, TensorFlow, XGBoost, etc.) and SQL.&nbsp;</li><li>Hands-on experience building and deploying ML models at scale in cloud environments (GCP Vertex AI, AWS SageMaker, Azure ML).&nbsp;</li><li>Experience with containerization (Docker, Kubernetes) and orchestration (Airflow, TFX, Kubeflow).&nbsp;</li><li>Familiarity with CI/CD pipelines, infrastructure-as-code (Terraform/Helm), and configuration management.&nbsp;</li><li>Experience with big data and streaming technologies (Spark, Flink, Kafka, Hive, Hadoop).&nbsp;</li><li>Practical exposure to model observability tools (Prometheus, Grafana, EvidentlyAI) and governance (WatsonX)&nbsp;</li><li>Strong understanding of statistical methods, ML algorithms, and deep learning architectures.&nbsp;</li></ul>

Responsibilities: Cloud Infrastructure Design and Deployment: <ul><li>Architect, design, and implement scalable, reliable, and secure solutions on GCP.</li><li>Deploy and manage GCP services in both development and production environments, ensuring seamless integration with existing infrastructure.</li><li>Implement and manage core services such as BigQuery, Datafusion, Cloud Composer (Airflow), Cloud Storage, Data Fusion, Compute Engine, App Engine, Cloud Functions and more.</li></ul> Infrastructure as Code (IaC) and Automation: <ul><li>Develop and maintain infrastructure as code using Terraform or CLI scripts to automate provisioning and configuration of GCP resources.</li><li>Establish and document best practices for IaC to ensure consistent and efficient deployments across environments.</li></ul> DevOps and CI/CD Pipeline Development: <ul><li>Create and manage DevOps pipelines for automated build, test, and release management, integrating with tools such as Jenkins, GitLab CI/CD, or equivalent.</li><li>Work with development and operations teams to optimize deployment workflows, manage application dependencies, and improve delivery speed.</li></ul> Security and IAM Management: <ul><li>Handle user and service account management in Google Cloud IAM.</li><li>Set up and manage Secrets Manager and Cloud Key Management for secure storage of credentials and sensitive information.</li><li>Implement network and data security best practices to ensure compliance and security of cloud resources.</li></ul> Performance Monitoring and Optimization: <ul><li>Monitoring &amp; Security: Set up observability tools like Prometheus, Grafana, and integrate security tools (e.g., SonarQube, Trivy).</li><li>Networking &amp; Storage: Configure DNS, networking, and persistent storage solutions in Kubernetes.</li><li>Set up monitoring and logging (e.g., Cloud Monitoring, Cloud Logging, Error Reporting) to ensure systems perform optimally.</li><li>Troubleshoot and resolve issues related to cloud services and infrastructure as they arise.</li></ul> Workflow Orchestration: <ul><li>Orchestrate complex workflows using Argo Workflow Engine.</li><li>Containerization: Work extensively with Docker for containerization and image management.</li><li>Optimization: Troubleshoot and optimize containerized applications for performance and security.</li></ul> Requirements:﻿ <ul><li>Bachelors or masters degree in computer science, Information Technology, or a similar field</li><li>Must have 10 + years of extensive experience in designing, implementing, and maintaining applications on GCP and OpenShift</li><li>Comprehensive expertise in GCP services such as GKE, Cloudrun, Functions, Cloud SQL, Firestore, Firebase, Apigee, GCP App Engine, Gemini Code Assist, Vertex AI, Spanner, Memorystore, Service Mesh, and Cloud Monitoring</li><li>Solid understanding of cloud security best practices and experience in implementing security controls in GCP</li><li>Thorough understanding of cloud architecture principles and best practices</li><li>Experience with automation and configuration management tools like Terraform and a sound understanding of DevOps principles</li><li>Proven leadership skills and the ability to mentor and guide a technical team</li></ul>

Role Description: <ul><li>Develop and own the data science roadmap that aligns with our client business goals.</li><li>Define success metrics and oversee the entire lifecycle of data initiatives from concept to production deployment.</li><li>Architect, develop, and deploy state-of-the-art machine learning models.</li><li>Champion best practices in model development, validation, and monitoring in production environments.</li><li>Partner with product, engineering, and business teams to translate business requirements into data-driven solutions.</li></ul> Responsibilities: <ul><li>Provide technical leadership and mentor the data science team.</li><li>Provide technical leadership and mentor the data science team.</li><li>Architect and provide production monitoring and support for our client ML services.</li><li>Stay ahead of industry trends by continuously evaluating emerging technologies and methodologies in data science and machine learning.</li><li>Represent our client technical excellence through publications, presentations, and participation in industry events.</li></ul> Requirements: <ul><li>10+ years of professional experience in data science with a proven track record in building and supporting production grade ML systems.</li><li>Must be highly proficient in Python with hands-on experience in machine learning frameworks (e.g., TensorFlow, PyTorch).</li><li>Experience working with large-scale datasets and distributed computing platforms like GCP.</li><li>Strong understanding of ML lifecycle and MLOps practices.</li><li>Experience with monitoring tools like Prometheus, Grafana and logging systems e.g. ELK stack.</li></ul>

Responsibilities: You will make an impact in the following way: Advanced Troubleshooting:&nbsp; <ul><li>Diagnose and resolve complex issues related to the cloud application, involving in-depth of the application architecture and infrastructure.</li></ul> Root Cause Analysis:&nbsp; <ul><li>Conduct thorough root cause analyses for recurring issues, implement preventive measures, and provide insights for continuous improvement.</li></ul> Automation and Scripting:&nbsp; <ul><li>Develop and utilize scripts and automation tools to streamline processes, improve efficiency, and reduce manual intervention in issue resolution.</li></ul> Documentation:&nbsp; <ul><li>Maintain comprehensive documentation for troubleshooting procedures, system configurations, and resolutions to facilitate sharing and future reference.</li></ul> Customer Communication:&nbsp; <ul><li>Effectively communicate with customers, providing clear and timely updates on issue resolution progress and collaborating on preventive measures.</li><li>You will be responsible for High Performance, Scalable, Robust, and Secure Common SaaS Framework based on AWS/OCP infrastructure.&nbsp;</li><li>This framework allows customers to configure provision, upgrade &amp; monitor various network functions in carrier grade enterprise &amp; telecom networks.&nbsp;</li></ul> Security and Compliance:&nbsp; <ul><li>Ensure the security of systems and applications, adhere to compliance requirements, and implement best practices for data protection.</li></ul> Collaboration:&nbsp; <ul><li>Collaboration with internal and external stakeholders.&nbsp;</li><li>Work closely with development, QA, and operations teams to foster a culture of collaboration and continuous improvement.&nbsp;</li><li>Collaborate with client’s team to gather the requirements.</li></ul> Training and Mentorship:&nbsp; <ul><li>Provide guidance and mentorship to junior team members and promote sharing within the organization.</li><li>Troubleshoot and fix any customer issues identified internally or in the production deployments.</li></ul> Requirements: <ul><li>5-8 years of Experience in DevOps (Jenkins, AWS services, Kafka, Grafana, elastic search, Bitbucket, Git, Deployment, Documentation, Root Cause Analysis reports, Customer interactions etc)</li><li>Experience with Cloud-native SaaS development &amp; virtualization technologies, for example, AWS, ESXi, KVM, HyperV would be a plus.&nbsp;</li><li>Extensive experience in developing distributed system design.</li><li>Prefer experience with microservice architecture, large scale, public cloud, IAM</li><li>Strong knowledge of containerization technologies like Kubernetes and Docker.&nbsp;</li><li>Strong knowledge in public cloud technologies (preferably AWS) and on-premises setups.</li><li>Good knowledge of analysing backend, frontend code whenever required and able to perform some tests as per customer issues.</li><li>Experience with CI/CD tooling like Jenkins etc.</li><li>Experience with version control system like Git, Bitbucket.</li><li>Experience with IaaC (Infrastructure as a Code) tools like Terraform.</li><li>Familiarity with agile development methodologies.&nbsp;</li><li>Lead and participate in various scrum ceremonies.</li><li>Good knowledge of observability tools like Kibana, Elastic stack, Prometheus stack etc.</li><li>Good knowledge of Linux operating system</li><li>Good knowledge of programming languages like Shell scripting, Python etc.</li><li>Knowledge of REST API, API gateways&nbsp;</li><li>Effective communication skills – written, spoken, listening and presentation.&nbsp;</li><li>Great Team player and experience working with global teams and global organizations.&nbsp;</li><li>Bachelor’s in computer science or equivalent.&nbsp;</li><li>Preferably a master’s degree in computer science.</li></ul>

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Senior Machine Learning Engineer (Production & MLOps Focus)

Senior DevOps Engineer

Principal Applied Data Scientist

Senior Devops Engineer

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Senior Machine Learning Engineer (Production & MLOps Focus)

Senior DevOps Engineer

Principal Applied Data Scientist

Senior Devops Engineer

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