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Artificial Intelligence Engineer

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Onsite

Artificial Intelligence Engineer

Solugenix

Artificial Intelligence Engineer

Skills

Data EngineeringDockerNodeMicroservicesPyTorchTensorFlowAmazon EC2AWS LambdaMachine LearningAIAWSKubernetes

1 month ago

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Important information


Contract type:

Permanent

Location:

Hyderabad, Telangana, India

Work mode:

Onsite

Published on:

21 September 2026

What they need


Role: AI Engineer

Job Location: Hyderabad

Experience: 5 to 10 years

Position Type: Full-time/Contract (Hybrid)

We are seeking an experienced AI Engineer to design, implement, and optimize enterprise-grade AI solutions. The ideal candidate will have a strong background in AI frameworks, backend development (Node.js), and cloud architecture (AWS). This role requires both hands-on technical expertise and the ability to provide architectural leadership across teams.

Key Responsibilities

Design scalable AI/ML systems leveraging modern frameworks (TensorFlow, PyTorch, Hugging Face, etc.).

Backend Development: Lead backend solution design and development using Node.js, ensuring performance, security, and maintainability.

Cloud Integration: Define and implement cloud-native architectures on AWS, including compute, storage, and AI/ML services.

Technical Leadership: Guide engineering teams on best practices, code reviews, and architectural decisions.

Innovation & Strategy: Evaluate emerging AI technologies and frameworks to recommend adoption strategies.

Collaboration: Work closely with product managers, data scientists, and DevOps teams to deliver end-to-end AI solutions

Required Skills:

Experience: 5 to 8 years in software engineering, with at least 2 to 4 years in AI/ML solution architecture.

AI/ML Frameworks: Proficiency in TensorFlow, PyTorch, Scikit-learn, Hugging Face, or similar.

Backend Development: Strong expertise in Node.js, RESTful APIs, and microservices architecture.

Cloud Awareness: Hands-on experience with AWS services (SageMaker, Lambda, EC2, S3, CloudFormation).

Architecture & Design: Proven ability to design scalable, secure, and high-performance systems.

Desired Skills:

Exposure to MLOps practices (CI/CD for ML, model deployment pipelines).

Knowledge of data engineering concepts (ETL, data lakes, streaming).

Experience with containerization (Docker, Kubernetes).

Familiarity with security best practices in AI and cloud environments

Soft Skills: Strong communication, leadership, and problem-solving abilities.

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