Machine Learning Engineer — MLOps & AI Infrastructure

Job Description (JD)

Company: Indium Software
Job Title: Machine Learning Engineer – MLOps & AI Infrastructure
Location: Hyderabad, Telangana
Office Address: Tablespace Building, 6th Floor, Building No. 4, Raheja Mindspace IT Park, HUDA Techno Enclave, HITEC City, Hyderabad, Telangana – 500081
Experience: 1–3 Years
CTC: ₹12–15 LPA
Employment Type: Full-time
Work Mode: On-site
Open Positions: 5–6 (proposed)

About the Company

Indium Software is a digital engineering company providing software development, data engineering, artificial intelligence, machine learning, and digital transformation solutions to businesses across multiple industries.

Job Summary

We are looking for a skilled Machine Learning Engineer specializing in MLOps and AI Infrastructure to develop, deploy, automate, and maintain scalable machine-learning systems. The ideal candidate should have hands-on experience with Python, ML pipelines, model deployment, containerization, and cloud-based machine-learning platforms.

The candidate will work closely with data scientists, software engineers, and DevOps teams to move machine-learning models from experimentation to reliable production environments.

Key Responsibilities

• Design, develop, and maintain end-to-end machine-learning pipelines.
• Automate model training, testing, validation, deployment, and retraining workflows.
• Implement experiment tracking, model versioning, and model registry using MLflow or similar tools.
• Build and manage ML workflows using Kubeflow, Apache Airflow, or equivalent orchestration tools.
• Containerize machine-learning applications using Docker and deploy them using Kubernetes.
• Develop CI/CD pipelines for automated testing and deployment of ML models.
• Deploy and manage ML solutions on cloud platforms such as AWS SageMaker and Azure Machine Learning.
• Monitor model performance, data drift, prediction accuracy, latency, and system reliability.
• Develop REST APIs to integrate machine-learning models with business applications.
• Optimize model-serving infrastructure for scalability, reliability, and cost efficiency.
• Work with data engineering teams to prepare data pipelines and ensure data quality.
• Implement logging, monitoring, testing, and reproducibility practices across ML workflows.
• Troubleshoot production issues and improve the performance of deployed AI/ML systems.

Required Technical Skills

• Strong proficiency in Python programming.
• Understanding of machine learning algorithms and model development workflows.
• Hands-on experience with MLflow or similar experiment-tracking and model-management tools.
• Knowledge of Docker and containerization.
• Familiarity with Kubernetes and container orchestration.
• Experience with CI/CD pipelines and automated deployment workflows.
• Knowledge of model deployment, model serving, and production monitoring.
• Understanding of REST APIs, SQL, Git, and software engineering fundamentals.
• Familiarity with cloud platforms such as AWS or Microsoft Azure.

Preferred Skills

• Experience with Kubeflow, Apache Airflow, or other workflow orchestration tools.
• Hands-on exposure to AWS SageMaker or Azure Machine Learning.
• Knowledge of model drift detection and model performance monitoring.
• Familiarity with feature stores and automated ML pipelines.
• Experience with infrastructure-as-code tools such as Terraform.
• Understanding of distributed training, scalable inference, and GPU-based workloads.
• Exposure to LLM deployment, inference optimization, and production AI infrastructure.
• Knowledge of monitoring tools such as Prometheus and Grafana.

Eligibility Criteria

• 1–3 years of relevant experience in machine learning engineering, MLOps, AI infrastructure, or ML deployment.
• Practical experience with at least one end-to-end ML deployment project.
• Strong debugging, analytical, and problem-solving skills.
• Ability to explain ML deployment architecture, pipeline automation, and production troubleshooting.
• Good communication and collaboration skills.

Ideal Candidate Profile

Candidates who have deployed machine-learning models into production, automated ML pipelines, implemented model versioning, and worked with Docker, Kubernetes, CI/CD, or cloud ML platforms will be preferred.

Candidates with practical MLOps projects, GitHub repositories, or experience resolving real-world model deployment issues will have an advantage.

Selection Process

• Resume screening and project evaluation.
• Python programming and technical assessment.
• Machine learning and MLOps technical interview.
• Practical assessment covering deployment pipelines, Docker, CI/CD, or model monitoring.
• Final technical and HR discussion.

Compensation and Recruitment Details

• CTC: ₹12–15 LPA (proposed; subject to company confirmation).
• Recruiter Payout: ₹55,000 per successful joining (subject to an agreed recruitment contract).
• Open Positions: 5–6 (proposed).
• Hiring Timeline: Immediate hiring; target closure within 30–45 days (proposed).

Important: The role specifications, salary range, hiring count, and recruiter payout are proposed mandate details, not confirmed live vacancies at Indium Software. Obtain company authorization and confirm these details before circulating the JD as an official opening.

Pay: ₹1,200,000.00 - ₹1,500,000.00 per year

Work Location: In person