AI ML Engineer

Saaki Argus And Averil Consulting; Chennai District; On-site; INR 1,500,000 - 3,000,000; Full time

Job summary

Saaki Argus And Averil Consulting invites an AI/ML Engineer in Chennai to design, build, and deploy production-grade ML models and data pipelines. You will work in a fast-paced environment across Python, TensorFlow, and modern ML tools, contributing to Gen AI initiatives and scalable ML solutions.

The role requires strong collaboration with cross-functional teams and hands-on implementation as well as architectural input, with experience in cloud platforms (AWS/GCP/Azure), MLOps practices, and

Qualifications

• Hands-on experience building production-grade ML models and pipelines.
• Strong Python and ML framework proficiency (TensorFlow, Keras, PyTorch).
• Experience with cloud platforms (AWS, GCP, Azure) and deployment tools.
• Familiarity with MLOps and ML lifecycle.
• Ability to work both independently and with global cross-functional teams.

Responsibilities

• Build and deploy production-grade ML models and pipelines.
• Understand ML lifecycle and MLOps.
• Collaborate with teams across geographies; contribute to both hands-on implementation and high-level architecture.
• Work with Git, Docker, Jenkins, Kubernetes, Kafka and CI/CD pipelines.
• Leverage cloud deployment and orchestration across AWS, GCP, or Azure.

Skills

Python

TensorFlow

Generative AI

Machine Learning

FastAPI

AWS

Tools

MLflow

Kubeflow

SageMaker

Vertex AI

Weights & Biases

DVC

Airflow

Prefect

Git

Docker

Jenkins

Kubernetes

Kafka

CI/CD

Job description

Job Title: AI/ML Engineer
Experience: 5-10 Years
Location: Chennai Work from Office, 5 Days a Week
Shift: General Shift

Key Skills: Python, TensorFlow, Generative AI, Machine Learning, AWS, FastAPI (Mandatory)

• We are looking for a Gen AI / Machine Learning Engineer with strong experience in Python, TensorFlow, Generative AI, Machine Learning, AWS, and FastAPI.
• The candidate should have experience in building and deploying production-grade ML models and pipelines, along with a good understanding of the ML lifecycle and MLOps.
• Hands‑on experience with tools such as MLflow, Kubeflow, AWS SageMaker, Vertex AI, Weights & Biases, DVC, Airflow, or Prefect is preferred.
• Experience with Git, Docker, Jenkins, Kubernetes, Kafka, and CI/CD pipelines is required.
• Knowledge of AWS, GCP, or Azure and cloud deployment and orchestration tools is expected. Experience with third‑party integrations is a plus.
• The candidate should be proficient in Python and ML frameworks such as TensorFlow, Keras, or PyTorch, with experience in statistical modeling, machine learning, data mining, unstructured data analytics, and data engineering pipelines.
• The role requires someone who can work independently and collaboratively with multi‑functional teams, principals, and architects across different geographies, while being comfortable with both hands‑on technical implementation and high‑level architecture.