The Role
Design, build, deploy, and maintain production-grade machine learning models and end-to-end pipelines. Perform feature engineering, model evaluation, optimization, REST API deployment, monitoring, and automated retraining. Collaborate with cross-functional teams to translate business requirements into scalable, reliable ML services using cloud, containerization, and CI/CD practices.
Summary Generated by Built In
What You'll Do
You will join our high-performance Data & AI engineering team and contribute to designing, developing, and deploying scalable Machine Learning solutions that power intelligent enterprise applications and AI-driven business outcomes.
- Develop, train, evaluate, and deploy Machine Learning models for enterprise and product use cases.
- Design and implement solutions for regression, classification, clustering, anomaly detection, and predictive analytics.
- Build reusable feature engineering, data preprocessing, and model training pipelines.
- Work with large-scale structured and unstructured datasets using Python and SQL.
- Collaborate with Data Engineers and AI Engineers to productionize Machine Learning models.
- Build end-to-end ML pipelines covering data ingestion, training, validation, deployment, inference, monitoring, and automated retraining.
- Deploy Machine Learning models as REST APIs using FastAPI, Flask, or similar frameworks.
- Optimize models for accuracy, scalability, reliability, and production performance.
- Monitor deployed models for prediction quality, model drift, and retraining requirements.
- Containerize ML applications using Docker and support automated deployments through CI/CD pipelines.
- Participate in architecture discussions, code reviews, technical documentation, and continuous improvement initiatives.
- Collaborate with Product Managers, Software Engineers, Data Engineers, and business stakeholders to deliver AI-driven solutions.
What We Seek In You
- 3+ years of experience in Machine Learning Engineering, Applied AI, or Data Science.
- Strong proficiency in Python, SQL, Scikit-learn, TensorFlow or PyTorch, NumPy, and Pandas.
- Strong understanding of supervised and unsupervised learning, regression, classification, clustering, anomaly detection, feature engineering, model evaluation, and hyperparameter tuning.
- Hands-on experience building and deploying Machine Learning models using FastAPI or Flask REST APIs.
- Experience working with Docker, CI/CD pipelines, Git, and cloud platforms such as Azure, AWS, or GCP.
- Good understanding of scalable ML systems, model optimization, monitoring, model drift detection, and retraining strategies.
- Strong analytical, debugging, and problem-solving skills.
- Excellent communication and stakeholder collaboration abilities.
- Ability to work effectively in agile, cross-functional engineering teams.
Preferred Qualifications
- Experience with MLflow, Kubeflow, Apache Airflow, Azure Machine Learning, or AWS SageMaker.
- Exposure to NLP, Computer Vision, or Recommendation Systems.
- Familiarity with Apache Spark or Hadoop.
- Knowledge of Responsible AI, Model Explainability, AI Fairness, Bias Detection, and AI Governance.
- Experience with Kubernetes or container orchestration platforms.
- Domain experience in Manufacturing, Automotive, Supply Chain, Financial Services, Healthcare, or Enterprise Analytics is an added advantage.
Life At Next
At our core, we're driven by the mission of tailoring growth for our customers by enabling them to transform their aspirations into tangible outcomes. We're dedicated to empowering them to shape their futures and achieve ambitious goals. To fulfil this commitment, we foster a culture defined by agility, innovation, and an unwavering commitment to progress. Our organizational framework is both streamlined and vibrant, characterized by a hands-on leadership style that prioritizes results and fosters growth.
Perks Of Working With Us
- Clear objectives to ensure alignment with our mission.
- Opportunities to collaborate closely with customers, product managers, and leadership.
- Continuous learning through Nexversity and structured mentorship.
- Hybrid work model promoting flexibility and work-life balance.
- Comprehensive family health insurance coverage.
- Accelerated career growth and opportunities to work with emerging AI technologies.
Who We Are
We enable high-growth enterprises to build hyper-personalized solutions that transform their vision into reality. With a keen eye for detail, we apply creativity, embrace new technology, and harness the power of Data and AI to co-create solutions tailored to our customers' unique needs.
Join our passionate team and tailor your growth with us!
Skills Required
- 3+ years of experience in Machine Learning Engineering or related domains
- Bachelor's or Master's degree in Computer Science, AI, Data Science, Statistics, Engineering, or related discipline
- Strong programming expertise in Python
- Strong SQL skills
- Understanding of supervised and unsupervised learning, regression, classification, clustering, anomaly detection, feature engineering, model evaluation, and hyperparameter tuning
- Hands-on experience with Scikit-learn, TensorFlow, and PyTorch
- Proficiency with NumPy and Pandas and working with large structured and unstructured datasets
- Experience deploying ML models as REST APIs using Flask or FastAPI
- Hands-on experience with Docker
- Experience with CI/CD pipelines and Git version control
- Experience with at least one cloud platform (Azure, AWS, or GCP)
- Experience building production-ready, scalable, reliable ML applications and pipelines
- Strong analytical thinking, debugging, problem-solving, communication, and collaboration skills
- Ability to translate business problems into practical AI and ML solutions and deliver maintainable production code
- Experience with MLOps platforms and tools (MLflow, Apache Airflow, Kubeflow, Azure ML, AWS SageMaker)
- Exposure to NLP, Computer Vision, or Recommendation Systems
- Familiarity with Big Data technologies (Apache Spark, Hadoop)
- Knowledge of model monitoring, data drift detection, and retraining strategies
- Understanding of Responsible AI, model explainability, fairness, and bias detection
- Experience with container orchestration and cloud-native deployment architectures
- Domain experience in Manufacturing, Automotive, Supply Chain, Quality Engineering, Financial Services, Healthcare, or Enterprise Analytics
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The Company
What We Do
TVS Next is a digital technology and consulting company that accelerates growth for clients through digital transformation and enterprise modernization solutions, leveraging software engineering, intelligence, and experience design.
