AI/ML Engineer

Agilisium Consulting; Chennai District; On-site; INR 1,000,000 - 1,500,000; Full time

Job summary

A leading technology consulting firm is seeking an experienced MLOps / GenAI Engineer to design and deploy production-grade ML pipelines. The role includes developing CI/CD pipelines, utilizing cloud-based solutions, and collaborating across teams to enhance AI/ML capabilities. Strong expertise in Python, Docker, Kubernetes, and various ML tools is essential. Candidates should demonstrate a robust understanding of the ML lifecycle and possess excellent problem-solving skills. This position is based in Chennai, Tamil Nadu, India.

Qualifications

• Strong proficiency in Python and ML frameworks such as TensorFlow, Keras, or PyTorch.
• Experience with MLOps tools such as MLFlow, Kubeflow, and AWS SageMaker.
• Deep understanding of the ML lifecycle and hands-on experience in productionizing ML models.

Responsibilities

• Design, build, and deploy production-grade ML pipelines using modern frameworks.
• Develop and manage CI/CD pipelines for ML model deployment.
• Implement containerization and orchestration for scalable model serving.

Skills

Python

MLOps

Generative AI

Statistical modeling

Problem-solving

Tools

TensorFlow

Keras

PyTorch

Docker

Kubernetes

MLFlow

Kubeflow

AWS SageMaker

Vertex AI

DVC

Airflow

Jenkins

Job description

We are seeking an experienced MLOps / GenAI Engineer with strong expertise in building and deploying production-grade ML pipelines, cloud-native solutions, and MLOps frameworks. The role requires a deep understanding of the ML lifecycle, CI/CD automation, containerization, and orchestration to deliver scalable, secure, and high‑performing AI/ML solutions in enterprise environments.

Accountabilities

• Design, build, and deploy production‑grade ML pipelines using modern frameworks and MLOps tools.
• Develop and manage CI/CD pipelines for ML model deployment and monitoring.
• Implement containerization (Docker) and orchestration (Kubernetes) for scalable model serving.
• Collaborate with data scientists, data engineers, and architects to productionize ML models.
• Ensure compliance with best practices for cloud‑based ML deployments across AWS, Azure, or GCP.
• Integrate third‑party services and APIs for enhanced solution capabilities.
• Contribute to architecture design while driving low‑level implementation.
• Work closely with cross‑functional teams across geographies to deliver end‑to‑end AI/ML solutions.
• Hands‑on experience in Generative AI and MLOps.
• Strong proficiency in Python and ML frameworks such as TensorFlow, Keras, or PyTorch.
• Experience with MLOps tools: MLFlow, Kubeflow, Weights & Biases, AWS SageMaker, Vertex AI, DVC, Airflow, Prefect.
• Proven experience in CI/CD pipelines, version control systems (Git), and deployment automation (Jenkins, Cloud Build, etc.).
• Strong knowledge of cloud platforms: AWS, GCP, Azure.
• Proficiency in containerization (Docker), Kubernetes, and Kafka.
• Strong background in statistical modeling, machine learning, and unstructured data analytics.
• Deep understanding of ML lifecycle and hands‑on experience in productionizing ML models.
• Exposure to third‑party integrations for AI/ML systems.
• Experience in architecture evolution for large‑scale AI/ML solutions.
• Ability to work both independently and collaboratively in distributed teams.
• Strong problem‑solving, stakeholder management, and technical communication skills.