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ML Engineer - India

About Aizen

At Aizen, we’re on a mission to make AI less complicated and more powerful. Our end-to-end platform streamlines the entire AI pipeline—from data ingestion and orchestration to model training, deployment, and monitoring—so businesses can focus on what really matters: building and scaling awesome AI solutions without the headache.

We started Aizen because we were tired of clunky AI workflows and patchwork solutions that hinder AI adoption. Today, companies of all sizes—from emerging startups to Fortune 500 enterprises—trust us to power their AI pipelines and scale effortlessly. By redefining how AI is built, deployed, and managed, we’re making AI more accessible and impactful than ever. If you love building cool tech, solving real-world problems, and working with a world-class engineering team, we’d love to meet you.

About the Team

Aizen was founded by a team of serial entrepreneurs with deep expertise in data storage, distributed systems, and real-time AI architecture. With experience building and scaling tech companies—some leading to successful $1B+ exits—our team understands what it takes to create a best-in-class product and company. We’ve designed Aizen from the ground up to simplify the AI pipeline, optimize performance, and make AI truly accessible to enterprises of all sizes.

About the Role

As a Machine Learning Engineer at Aizen, you’ll be instrumental in building and optimizing the AI pipelines that power our end-to-end AI platform. You’ll work across the stack, from data ingestion and model training to real-time inference and monitoring, ensuring seamless AI deployment at scale. Whether you’re an entry-level engineer eager to grow or a senior engineer ready to lead high-impact projects, this role offers the opportunity to tackle complex ML challenges, enhance automation, and help shape the future of AI infrastructure.

Core Responsibilities

  • AI Pipeline Development – Design, build, and optimize end-to-end AI pipelines for data ingestion, training, deployment, and real-time inference, ensuring seamless integration with MLOps and infrastructure systems.

  • Model Training & Deployment – Implement training and fine-tuning workflows for ML models, optimizing for efficiency, scalability, and reproducibility across cloud, on-prem, and edge environments.

  • Backend & API Development – Develop and integrate scalable backend services and APIs for model inference, batch processing, and real-time AI applications, collaborating with data and product teams.

  • Observability & Model Monitoring – Build monitoring and logging tools to track model performance, drift, and latency, developing automated alerts and continuous retraining workflows to maintain AI accuracy.

  • LLM Development & Optimization – Build and deploy LLM-based applications, integrating APIs from providers like OpenAI, Anthropic, and Cohere, and optimizing for fine-tuning, inference, and cost efficiency.

  • Advanced AI & ML Development – Research and implement cutting-edge ML techniques, experimenting with LLM fine-tuning, embeddings, and model architectures to enhance AI performance and efficiency.

Preferred Qualifications (Applicable to All Levels)

  • Proficiency in Python and ML frameworks – Strong coding skills in Python, with experience using frameworks such as PyTorch, TensorFlow, or JAX for ML and DL applications.

  • Cloud & MLOps Experience – Familiarity with cloud platforms like AWS, GCP, or Azure and experience deploying and managing ML models in production environments.

  • Strong Problem-Solving & Collaboration Skills – Ability to tackle complex ML challenges, iterate quickly, and work closely with cross-functional teams including engineering, product, and data science.

  • Understanding of MLOps & Model Deployment – Experience with CI/CD for ML, model serving, and monitoring, using tools like MLflow, Kubernetes, or Ray.

  • Experience with LLMs & NLP (Nice to Have) – Knowledge of transformer-based models, fine-tuning techniques, and APIs from OpenAI, Anthropic, or Cohere is a plus.

Entry-Level Qualifications

  • 1+ Year(s) of Experience in AI/ML Development – Experience with training and deploying ML models, either in a professional setting, academic setting, research projects, or internships.

  • Experimental Mindset & Eagerness to Learn – Comfortable iterating on models, testing different approaches, and staying up-to-date with state-of-the-art AI advancements.

Senior-Level Qualifications

  • 4+ Years of Experience in AI/ML Engineering – Proven ability to ship ML/AI capabilities to production, optimizing for scalability, latency, and reliability.

  • Experience Building AI/ML Infra at Scale – Background in distributed systems, real-time inference, and MLOps automation for enterprise-level AI applications.

  • Technical Leadership & Mentorship – Ability to drive architectural decisions, mentor junior engineers, and lead cross-functional initiatives to enhance AI capabilities at scale.

Benefits

At Aizen, we believe great work starts with taking care of our team. We offer competitive compensation, flexible work options, and comprehensive benefits to support you both personally and professionally.

  • Competitive Compensation – We offer a competitive salary along with meaningful equity, so you can directly share in Aizen’s success.

  • Remote-Friendly Culture – Work from wherever you’re most productive, with the option to collaborate in person at our office hubs.

  • Flexible PTO – Take the time you need with our generous paid time off policy—because work-life balance matters.

  • Comprehensive Health Plans – We provide medical, dental, and vision coverage for you and your family.

  • Paid Parental Leave – Enjoy fully paid parental leave to spend time with your growing family.

  • 401(k) Plan (U.S. Specific) – We help you plan for the future with a company-supported 401(k).

Equal Opportunity

Aizen is an equal opportunity employer committed to fostering a diverse and inclusive workplace. We welcome applicants of all backgrounds and do not discriminate based on race, gender, age, disability, or any other protected status.

Average salary estimate

$90000 / YEARLY (est.)
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$60000K
$120000K

If an employer mentions a salary or salary range on their job, we display it as an "Employer Estimate". If a job has no salary data, Rise displays an estimate if available.

What You Should Know About ML Engineer - India, Aizen

Are you ready to take your career to the next level as a Machine Learning Engineer at Aizen in Hyderabad? At Aizen, we’re on a mission to simplify AI, and we need innovative minds like yours to help us achieve that. You’ll get to design, build, and optimize our AI pipelines, ensuring smooth operations at every step—from data ingestion and model training to real-time deployment and monitoring. Our team is composed of seasoned entrepreneurs and engineers, all passionate about leveraging AI to solve real-world challenges. Whether you're an early-career engineer hungry to learn or a seasoned professional ready to lead initiatives, this role offers a chance to tackle complex ML issues and influence the future of AI infrastructure. You’ll become part of our journey to democratize AI, making it accessible for companies of all sizes. With a strong emphasis on collaboration, you’ll also work closely with cross-functional teams to build scalable backend services and APIs that power real-time AI applications. Plus, you’ll have the opportunity to experiment with cutting-edge ML techniques, making a tangible impact on performance and efficiency. Join us at Aizen, where your contributions will help shape a more intelligent future!

Frequently Asked Questions (FAQs) for ML Engineer - India Role at Aizen
What responsibilities does a Machine Learning Engineer at Aizen have?

As a Machine Learning Engineer at Aizen, you'll be responsible for designing, building, and optimizing AI pipelines that support our entire platform. Your duties will include model training and deployment across various environments, backend development for real-time applications, and monitoring tools to ensure our systems continually perform at their best. You'll work closely with teams across the company to ensure seamless AI integration and operational efficiency.

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What qualifications are needed for the Machine Learning Engineer position at Aizen?

For the Machine Learning Engineer role at Aizen, we prefer candidates with proficiency in Python and familiarity with ML frameworks like PyTorch or TensorFlow. Entry-level candidates should have at least 1 year of experience, while more senior roles require 4+ years in AI/ML engineering, including experience with MLOps and deployment. Understanding of cloud environments like AWS or GCP is also preferable.

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What should I expect as a workload for a Machine Learning Engineer at Aizen?

As a Machine Learning Engineer at Aizen, you can expect a dynamic workload involving a mix of hands-on development, experimentation with new ML techniques, and cross-team collaboration. You'll be tackling complex challenges in AI infrastructure, building scalable solutions, and constantly optimizing our AI systems, lending a balanced experience through both individual projects and collaborative initiatives.

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Does Aizen provide opportunities for professional growth for Machine Learning Engineers?

Absolutely! Aizen fosters a supportive environment that emphasizes learning and growth. As a Machine Learning Engineer, you'll have the chance to lead high-impact projects, experiment with advanced techniques, and mentor junior engineers, all contributing to your professional development in the fast-evolving field of AI.

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What kind of projects will I work on as a Machine Learning Engineer at Aizen?

In your role as a Machine Learning Engineer at Aizen, expect to work on diverse and impactful projects that involve developing end-to-end AI pipelines, deploying LLM applications, and enhancing automated monitoring systems. You'll also explore the cutting edges of AI, experimenting with various models and techniques, directly influencing the functionality and efficiency of our product offerings.

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Common Interview Questions for ML Engineer - India
Can you describe your experience with end-to-end AI pipeline development?

When answering this question, focus on specific projects where you designed, developed, and optimized AI pipelines. Highlight any tools, frameworks, and methods you used, and illustrate how your contributions led to improved performance or efficiency.

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What machine learning frameworks are you proficient in?

Name the frameworks you have experience with, such as TensorFlow, PyTorch, or JAX. Discuss projects where you've applied these frameworks effectively, and provide examples of how you utilized their features to solve specific problems or enhance model performance.

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How do you ensure the quality and performance of machine learning models in production environments?

Discuss your knowledge of MLOps practices, including CI/CD for ML, monitoring, and alert systems. Explain how you track model drift, evaluate performance metrics, and implement retraining workflows to maintain model accuracy over time.

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What has been your involvement in LLM development?

Share specific examples of any LLM projects you've worked on, including data preprocessing, model fine-tuning, or application deployment. Discuss the APIs you've used, like those from OpenAI or Anthropic, and how you optimized implementations for better performance.

Join Rise to see the full answer
How do you approach collaboration with cross-functional teams?

Illustrate your teamwork and collaboration skills, mentioning specific instances where you worked alongside data scientists, product managers, or engineers. Emphasize your proactive communication style and how it contributed to project success.

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Can you explain a challenging machine learning problem you faced and how you solved it?

Provide a clear narrative of the challenge, the measures you took to address it, and the final outcome. Highlight your problem-solving skills and innovative thinking, showcasing your ability to debug and iterate on models.

Join Rise to see the full answer
What role do you see cloud platforms playing in ML model deployment?

Discuss the benefits of cloud platforms like AWS, GCP, and Azure for machine learning, including scalability, access to advanced tools, and ease of integration with existing infrastructure. You can also mention your personal experiences handling deployments in these environments.

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How do you keep your knowledge of AI and ML up to date?

Share your strategies for staying informed about the latest trends and techniques in AI and ML, such as following industry blogs, participating in online courses, or engaging with professional communities. This shows your commitment to continuous learning.

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Describe your coding style and testing practices in Python.

Address your approach to writing clean, efficient code and your testing protocols, such as unit and integration tests. Illustrate your process with examples that showcase best practices in Python for ML projects.

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Why do you want to work at Aizen, and what do you think you can contribute?

Express your enthusiasm for Aizen’s mission to simplify AI. Highlight your skills and experiences that align with the job description and how you believe you can help Aizen achieve its goals, contributing to building a more effective AI pipeline.

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DATE POSTED
February 26, 2025

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