Industry Data Research

**Job Title**: Machine Learning Engineer

**Location**: Hybrid

**Job Type**: Full-time

**About Us**:
Ralliant is at the forefront of leveraging data and AI technologies to drive innovation across Precision Tech Industries. We aim to solve real-world challenges by combining advanced machine learning, cloud computing, and generative AI to deliver cutting-edge solutions. We’re looking for a talented and passionate** MLE** to join our dynamic team and help us continue to push the boundaries of AI.
- **Model Deployment & MLOps**:

- 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.
- **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