Machine Learning Engineer (Freelancer)

Soul Ai; New Delhi; Hybrid; INR 1,500,000 - 2,500,000; Part time

Job summary

Soul AI invites top-tier ML engineers to design, deploy and optimize AI models for production, collaborating with industry leaders in an expert community.

Contract-based engagement with project timelines from 2–6 months or freelance arrangements, with work locations including remote options and client sites in India. Join an elite group tackling complex AI challenges with cutting-edge tools and practices.

Qualifications

• Expertise in ML, DL, and data engineering.
• Experience deploying ML models to production.
• Familiarity with security and regulatory compliance.

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.

Skills

Machine Learning

Deep Learning

Data Engineering

MLOps

Cloud Platforms

Tools

TensorFlow

PyTorch

Docker

Kubernetes

Kubeflow

MLflow

TFX

Job description

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

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. If you have experience in this field then this is your chance to collaborate with industry leaders.

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.

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).