Machine Learning Engineer (Freelancer)

Soul Ai; Delhi; Hybrid; INR 1,562,000 - 2,344,000; Full time

Job summary

Soul AI, the Experts Community by Deccan AI, invites India's top 1% Machine Learning Engineers for a contract opportunity to design, deploy, and optimize AI models.

Roles are project-based with timelines of 2–6 months, offering above-market compensation. Remote work is likely with onsite options at client locations in Hyderabad or Bangalore, or hybrid arrangements as required.

Qualifications

• Proficiency in ML, DL, and data engineering.
• Experience with MLOps, automation tools, and cloud platforms.
• Strong knowledge of model deployment, monitoring, security, and governance.

Responsibilities

• Design, build, and deploy ML models and pipelines.
• Implement feature stores, CI/CD for retraining and monitoring.
• Ensure security, compliance, and scalable production workflows.

Skills

Machine Learning

Deep Learning

Data Engineering

Tools

Docker

Kubernetes

Kubeflow

MLflow

TFX

Spark

Kafka

AWS

GCP

Azure

Job description

Step into the world of AI innovation with the Experts Community of Soul AI (By Deccan AI).

We are looking for Indias top 1% Machine Learning Engineers for a unique job opportunity to work with the industry leaders.

Who can be a part of the community.

We are looking for top-tier Machine Learning Engineers with expertise in building, deploying, and optimizing AI models.

Whats in it for you.

Pay above market standards.

The role is going to be contract based with project timelines from 2-6 months, or freelancing.

Be a part of Elite Community of professionals who can solve complex AI challenges.

Work location could be:

Remote (Most likely).

Onsite on client location.

Deccan AIs OfficeHyderabad or Bangalore.

Responsibilities.

• Design, optimize, and deploy machine learning models; implement feature engineering and scaling pipelines.
• Use deep learning frameworks (TensorFlow, PyTorch) and manage models in production (Docker, Kubernetes).
• Automate workflows, ensure model versioning, logging, and real-time monitoring; comply with security and regulations.
• Work with large-scale data, develop feature stores, and implement CI/CD pipelines for model retraining and performance tracking.

Required

Skills:

• Proficiency in machine learning, deep learning, and data engineering (Spark, Kafka).
• Expertise in MLOps, automation tools (Docker, Kubernetes, Kubeflow, MLflow, TFX), and cloud platforms (AWS, GCP, Azure).
• Strong knowledge of model deployment, monitoring, security, compliance, and responsible AI practices.

Nice to Have.

• Experience with A/B testing, Bayesian optimization, and hyperparameter tuning.
• Familiarity with multi-cloud ML deployments and generative AI technologies (LLM fine-tuning, FAISS).