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 team and play a key role in designing, developing, deploying, and maintaining enterprise-grade Machine Learning solutions that enable intelligent decision-making, predictive analytics, and AI-driven business transformation.
- Design, develop, and deploy scalable machine learning models for enterprise and product-focused use cases.
- Build machine learning solutions for:
- Regression
- Classification
- Clustering
- Anomaly Detection
- Predictive Analytics
- Evaluate, validate, optimize, and fine-tune machine learning models to improve performance, accuracy, scalability, and reliability.
- Collaborate with Data Scientists and AI Engineers to productionize machine learning models and improve model lifecycle management.
- Perform data preprocessing, cleansing, transformation, and feature engineering for structured and unstructured datasets.
- Develop reusable feature engineering pipelines and datasets for training, validation, and inference.
- Work with large-scale datasets using Python, SQL, and modern data processing frameworks.
- Identify and resolve data quality issues while ensuring data consistency and governance.
- Build and maintain end-to-end machine learning pipelines covering data ingestion, model training, validation, deployment, inference, monitoring, and automated retraining.
- Deploy machine learning models as REST APIs and scalable services using Flask, FastAPI, or equivalent frameworks.
- Ensure deployed models meet enterprise standards for latency, scalability, reliability, availability, and cost optimization.
- Utilize Docker and CI/CD pipelines to enable automated and repeatable model deployment.
- Monitor model performance, prediction quality, model drift, and retraining requirements in production environments.
- Optimize machine learning services for performance, maintainability, scalability, and operational efficiency.
- Troubleshoot production issues related to machine learning models, APIs, inference services, and data pipelines.
- Develop clean, reusable, maintainable, and well-documented production-ready code.
- Collaborate with Product Managers, Software Engineers, Data Engineers, and Business Stakeholders to translate business requirements into AI-driven solutions.
- Participate in architecture discussions, code reviews, technical documentation, and continuous improvement initiatives.
- Stay updated with emerging technologies in Artificial Intelligence, Machine Learning, Cloud Computing, and MLOps.
What We Seek In You
- 3+ years of experience in Machine Learning Engineering, Artificial Intelligence, Applied Data Science, or related domains.
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Statistics, Engineering, or a related discipline.
- Strong programming expertise in:
- Python
- SQL
- Strong understanding of Machine Learning concepts including:
- Supervised Learning
- Unsupervised Learning
- Regression
- Classification
- Clustering
- Anomaly Detection
- Feature Engineering
- Model Evaluation
- Hyperparameter Tuning
- Hands-on experience with Machine Learning frameworks including:
- Scikit-learn
- TensorFlow
- PyTorch
- Strong experience working with:
- NumPy
- Pandas
- SQL
- Large-scale structured and unstructured datasets
- Experience deploying machine learning models using:
- Flask
- FastAPI
- REST APIs
- Hands-on experience with:
- Docker
- CI/CD Pipelines
- Git Version Control
- Experience working with at least one cloud platform:
- Microsoft Azure
- Amazon Web Services (AWS)
- Google Cloud Platform (GCP)
- Strong analytical thinking, debugging, and problem-solving capabilities.
- Experience building scalable, reliable, and production-ready machine learning applications.
- Strong communication and collaboration skills with the ability to work effectively across cross-functional teams.
- Ability to translate business problems into practical AI and Machine Learning solutions.
- Strong ownership mindset with attention to quality, scalability, and maintainability.
Preferred Qualifications
- Experience with MLOps platforms and tools including:
- MLflow
- Apache Airflow
- Kubeflow
- Azure Machine Learning
- AWS SageMaker
- Exposure to:
- Natural Language Processing (NLP)
- Computer Vision
- Recommendation Systems
- Familiarity with Big Data technologies including:
- Apache Spark
- Hadoop
- Knowledge of:
- Model Monitoring
- Data Drift Detection
- Retraining Strategies
- Production Machine Learning Operations
- Understanding of:
- Responsible AI
- Model Explainability
- AI Fairness
- Bias Detection
- AI Governance
- Experience with container orchestration platforms and cloud-native deployment architectures.
- Domain experience in Manufacturing, Automotive, Supply Chain, Quality Engineering, 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, fostering your meaningful contribution.
- Abundant opportunities for engagement with customers, product managers, and leadership.
- You'll be guided by progressive paths while receiving insightful guidance from managers through ongoing feedforward sessions.
- Cultivate and leverage robust connections within diverse communities of interest. Choose your mentor to navigate your current endeavors and steer your future trajectory.
- Embrace continuous learning and upskilling opportunities through Nexversity.
- Enjoy the flexibility to explore various functions, develop new skills, and adapt to emerging technologies. Embrace a hybrid work model promoting work-life balance.
- Access comprehensive family health insurance coverage, prioritizing the well-being of your loved ones.
- Embark on accelerated career paths to actualize your professional aspirations.
Who we are?
We enable high growth enterprises build hyper personalized solutions to 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 made to meet unique needs for our customers.
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.
