Industry Data Research & Validation P17
- Intelex Technologies
- 1 - 4 years
- Bengaluru
- 2 months ago
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Job Description
- Design, build, and maintain machine learning pipelines, ensuring continuous integration and deployment (CI/CD) of models in production environments.
- Deploy machine learning models as APIs, microservices, or serverless functions for real-time inference.
- Manage and scale machine learning workloads using Kubernetes, Docker, and cloud-based infrastructure (AWS, Azure, GCP).
Automation & Scripting:
- Automate routine tasks across the ML lifecycle (data preprocessing, model training, evaluation, deployment) using Python, Bash, and other scripting tools.
- Implement automation for end-to-end model management and monitor pipelines for health, performance, and anomalies.
Cloud Platforms & Infrastructure:
- Utilize cloud platforms (AWS, Azure, GCP) to optimize the scalability, performance, and cost-effectiveness of ML systems.
- Leverage Infrastructure as Code (IaC) tools like Terraform or CloudFormation to provision and manage cloud resources effectively.
Data Pipelines & Integration:
- Build and maintain robust data pipelines to streamline data ingestion, preprocessing, and feature engineering.
- Work with both structured and unstructured data sources and databases (SQL, NoSQL) to feed data into ML models.
Monitoring, Logging & Troubleshooting:
- Set up monitoring and logging systems to track model performance, detect anomalies, and maintain system health.
- Diagnose and resolve issues across the machine learning pipeline and deployed models.
Collaboration & Communication:
- Collaborate closely with data scientists, software engineers, and business stakeholders to ensure machine learning models meet the required business objectives and performance standards.
- Effectively communicate complex ML concepts and technical details to non-technical stakeholders.
GenAI & AI Agents Expertise:
- Stay up to date with the latest trends in Generative AI (e.g., GPT models, Diffusion models) and AI agents, and bring this expertise into production environments.
- Design and deploy advanced GenAI solutions, ensuring they are aligned with business needs and ethical AI principles.
Security & Compliance:
- Implement robust security measures for machine learning models and ensure compliance with relevant data protection and privacy regulations.
- Address vulnerabilities, ensuring safe and secure deployment of models in production environments.
Optimization & Cost Management:
- Optimize machine learning resources (compute, memory, storage) to achieve high performance while minimizing operational costs.
- Regularly review and improve the efficiency of machine learning workflows.
Testing & Validation:
- Develop and execute rigorous testing and validation strategies to ensure the reliability, accuracy, and fairness of deployed models.
- Use automated testing frameworks to continuously validate model performance.
Required Skills & Qualifications:
- Education : Bachelor s or Master s degree in Computer Science, Engineering, Data Science, or a related field.
- Experience :
- Proven experience (3+ years) in machine learning engineering, MLOps, or related fields.
- Experience with deploying and managing machine learning models in production using tools like Kubernetes, Docker, and CI/CD pipelines.
- Hands-on experience with cloud platforms (AWS, Azure, GCP) and infrastructure automation tools (Terraform, CloudFormation).
- Strong coding experience in Python, Bash, or other scripting languages.
- Expertise in Generative AI models (e.g., GPT, GANs) and their deployment at scale.
- Experience working with databases (SQL, NoSQL) and building data pipelines.
- DevOps & CI/CD :
- Knowledge of DevOps tools and practices, including version control (Git), automated testing, and continuous integration/deployment.
- AI Agents : Familiarity with the latest AI agent frameworks and their deployment in real-world applications.
- Data Science Concepts : Solid understanding of GenAI,NLP, Computer Vision, machine learning algorithms, data structures, and model evaluation techniques.
- Problem-Solving : Strong troubleshooting and debugging skills to quickly identify and fix issues within ML pipelines and deployments.
- Collaboration & Communication : Excellent communication skills with the ability to work in a cross-functional team and explain technical concepts to non-technical stakeholders.
Preferred Qualifications:
- Certification in Cloud Technologies (AWS, Azure, GCP) and MLOps platforms.
- Experience with large-scale ML systems and distributed computing.
- Understanding of ethical AI practices and AI fairness.
- Familiarity with cutting-edge AI technologies like reinforcement learning, AI agents, and deep learning.
Technical Skills:
- Proficiency in Python, R, or other relevant programming languages.
- Strong knowledge of machine learning libraries such as TensorFlow, PyTorch, Scikit-learn, or Keras.
- Experience with SQL and cloud-native data processing tools (e.g., AWS Redshift, Azure Synapse, Spark).
- Familiarity with DevOps practices and CI/CD pipelines for ML model deployment.
Soft Skills:
- Strong communication skills with the ability to translate complex technical concepts into business-friendly language.
- Problem-solving mindset, with the ability to approach challenges creatively and collaborate with diverse teams.
- Leadership potential or experience mentoring junior team members.
Preferred Qualifications:
- Certification or training in AWS (e.g., AWS Certified Machine Learning), Azure, or other cloud services.
- Experience working with containerization technologies like Docker and Kubernetes for model deployment.
- Exposure to the latest trends in AI ethics, explainability, and fairness.
Job Classification
Industry: Software ProductFunctional Area / Department: Engineering - Software & QARole Category: Software DevelopmentRole: Data Platform EngineerEmployement Type: Full timeContact Details:
Company: Intelex TechnologiesLocation(s): Bengaluru+ View Contact
Keyskills: Computer science Automation Coding Healthcare Data processing Workflow Troubleshooting Continuous improvement Operations SQL
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Trusted Since 1992, Intelex Technologies, ULC. is a global leader in the development and support of software solutionsfor Environment, Health, Safety and Quality (EHSQ) programs. Our scalable, web-based software provides clients withunprecedented flexibil
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