Ai engineer- Genrative Ai at Weekday AI

Chennai, tamil nadu, India -

Full Time

Start Date

Immediate

Expiry Date

27 Feb, 26

Salary

3500000.0

Posted On

29 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Machine Learning, TensorFlow, PyTorch, Scikit-learn, Google Cloud Platform, Vertex AI, Model Evaluation, Data Preparation, Feature Engineering, Model Deployment, Collaboration, Problem Solving, Documentation, Continuous Improvement, Scalability

Industry

technology;Information and Internet

Description

This role is for one of the Weekday's clients Salary range: Rs 2000000 - Rs 3500000 (ie INR 20-35 LPA) Min Experience: 5 years Location: Hyderabad, Chennai JobType: full-time As an AI/ML Engineer, you will be responsible for creating end-to-end machine learning solutions—from data exploration to model deployment. You will work closely with cross-functional teams to understand requirements, develop ML pipelines, test model performance, and integrate solutions into production environments. Your responsibilities will involve leveraging Python, ML libraries, TensorFlow, PyTorch, and cloud tools to build efficient, scalable AI systems. Experience with Google Cloud Platform, particularly Vertex AI, will enable you to accelerate model development, orchestration, and deployment. You will also collaborate with peers, contribute to architectural decisions, and troubleshoot model or pipeline issues to ensure reliable outcomes. Key Responsibilities Model Development: Build, train, and optimize AI/ML models using frameworks such as TensorFlow, PyTorch, and Scikit-learn. End-to-End ML Pipeline: Design and implement complete machine learning workflows including data preparation, feature engineering, training, testing, and deployment. Cloud-Based AI Work: Work with Google Cloud Platform services and tools for scalable model training, storage, monitoring, and deployment. Vertex AI Implementation: Utilize Vertex AI (if experienced) for automated ML, model registry, feature store, pipelines, and real-time deployment. Model Evaluation: Perform model testing, validation, and tuning to ensure accuracy, reliability, and performance. Collaboration: Work with data engineers, product teams, and developers to integrate ML models into products or applications. Problem Solving: Analyze technical challenges, debug issues, and propose innovative solutions based on best practices. Documentation: Maintain detailed documentation for model logic, workflows, and deployment guidelines. Continuous Improvement: Research emerging AI technologies, frameworks, and cloud capabilities to improve existing systems. Scalability & Optimization: Ensure models and pipelines are efficient, secure, maintainable, and scalable for long-term use. What Makes You a Great Fit Technical Expertise: Strong programming experience in Python and proficiency with ML frameworks like TensorFlow, PyTorch, and Scikit-learn. AI Knowledge: Solid understanding of machine learning algorithms, training techniques, evaluation metrics, and deployment best practices. Cloud Experience: Familiarity or hands-on experience with Google Cloud Platform and an interest in working extensively with Vertex AI. Analytical Thinking: Strong problem-solving abilities and the ability to handle complex datasets and ML challenges. Independent & Team-Oriented: Comfortable working independently and collaborating within a team environment. Communication Skills: Ability to explain technical concepts clearly to stakeholders and document processes concisely. Adaptability: Openness to learning new tools, exploring new technologies, and evolving with project requirements. Educational Foundation: Bachelor’s degree in Computer Science, Artificial Intelligence, or a related field supports strong theoretical grounding. Ownership Mindset: You take initiative, deliver high-quality work, and ensure model performance meets business needs.

Responsibilities

You will be responsible for creating end-to-end machine learning solutions, including data exploration and model deployment. Collaborating with cross-functional teams, you will develop ML pipelines and integrate solutions into production environments.