The Role
Design, deploy, monitor, and maintain production machine learning systems and end-to-end ML pipelines. Build scalable MLOps frameworks, automate deployments and retraining, manage cloud-native infrastructure and CI/CD, implement IaC, containerization, monitoring, and governance, and collaborate with cross-functional teams to ensure reliable, compliant enterprise AI platforms.
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, deploying, monitoring, and maintaining enterprise-grade machine learning solutions. You will bridge the gap between Data Science, AI Engineering, and Cloud Operations by building scalable MLOps platforms, automating ML workflows, and ensuring reliable production AI systems.
- Design, build, deploy, and maintain machine learning models in production environments.
- Develop and manage end-to-end machine learning pipelines covering data ingestion, feature engineering, model training, validation, deployment, monitoring, and retraining.
- Build scalable MLOps frameworks that enable efficient model lifecycle management across enterprise AI platforms.
- Implement model versioning, experiment tracking, governance, and reproducibility best practices.
- Automate model deployment, retraining, rollback, and release workflows.
- Design and manage cloud-native infrastructure supporting enterprise AI and machine learning workloads.
- Develop and maintain CI/CD pipelines for machine learning applications and AI services.
- Implement Infrastructure as Code (IaC) using Terraform, ARM Templates, Bicep, or equivalent technologies.
- Deploy and manage containerized AI applications using Docker and Kubernetes.
- Monitor model performance, prediction quality, data drift, concept drift, system health, and resource utilization.
- Troubleshoot production issues related to ML pipelines, model serving, infrastructure, and deployment workflows.
- Implement logging, monitoring, alerting, and observability solutions for AI platforms.
- Optimize model serving performance, scalability, latency, and infrastructure efficiency.
- Collaborate with Data Scientists, AI Engineers, Software Developers, DevOps Engineers, and Business Stakeholders to operationalize machine learning solutions.
- Support AI governance, model security, compliance, audit readiness, and enterprise AI standards.
- Document MLOps processes, deployment architectures, operational runbooks, and engineering best practices.
- Participate in architecture reviews and continuously improve AI platform capabilities using emerging cloud-native technologies.
What We Seek In You
- 3+ years of experience in MLOps, Machine Learning Engineering, DevOps, Cloud Engineering, or AI Platform Engineering.
- Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Information Technology, Engineering, or a related discipline.
- Strong programming expertise in:
- Python
- SQL
- Bash / Shell Scripting
- PowerShell (preferred)
- Strong understanding of:
- Machine Learning Lifecycle Management
- Model Deployment
- Model Serving
- Feature Engineering Concepts
- Experiment Tracking
- Model Governance
- Hands-on experience with MLOps platforms including:
- MLflow
- Azure Machine Learning
- AWS SageMaker
- Google Vertex AI
- Git
- GitHub
- GitLab
- Azure DevOps
- Strong expertise in cloud-native DevOps and infrastructure technologies including:
- Docker
- Kubernetes
- CI/CD Pipelines
- Terraform
- Microsoft Azure
- Amazon Web Services (AWS)
- Google Cloud Platform (GCP)
- Experience working with enterprise databases and data platforms including:
- SQL Server
- PostgreSQL
- MySQL
- Data Lakes
- Data Warehouses
- ETL / ELT Pipelines
- Hands-on experience implementing monitoring and observability solutions using:
- Prometheus
- Grafana
- Azure Monitor
- Cloud-native monitoring platforms
- Strong understanding of IAM, cloud security, infrastructure security, and enterprise governance practices.
- Experience deploying and supporting production-scale Machine Learning systems.
- Strong analytical thinking, troubleshooting, and problem-solving capabilities.
- Excellent communication, documentation, and stakeholder management skills.
- Ability to collaborate effectively across Data Science, AI Engineering, Cloud Infrastructure, and DevOps teams.
- Strong ownership mindset with the ability to manage multiple priorities and deliver high-quality AI platforms.
Preferred Qualifications
- Experience with Generative AI and Large Language Models (LLMs).
- Knowledge of Retrieval-Augmented Generation (RAG) and enterprise knowledge retrieval solutions.
- Experience with:
- OpenAI
- Azure OpenAI
- Claude
- Gemini
- Other foundation model platforms
- Familiarity with AI orchestration frameworks including:
- LangChain
- Semantic Kernel
- AutoGen
- CrewAI
- Experience with distributed data processing and orchestration platforms including:
- Apache Airflow
- Prefect
- Apache Spark
- Databricks
- Knowledge of Vector Databases including:
- Pinecone
- Weaviate
- FAISS
- ChromaDB
- Exposure to Responsible AI, Explainable AI, AI Governance, and Model Explainability frameworks.
- Experience working in Manufacturing, Automotive, Healthcare, Financial Services, Supply Chain, or Enterprise AI domains is highly preferred.
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 MLOps, Machine Learning Engineering, DevOps, Cloud Engineering, or AI Platform Engineering
- Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Information Technology, Engineering, or related discipline
- Strong programming expertise in Python
- Strong programming expertise in SQL
- Strong programming expertise in Bash / Shell Scripting
- PowerShell
- Strong understanding of Machine Learning Lifecycle Management, Model Deployment, Model Serving, Feature Engineering, Experiment Tracking, and Model Governance
- Hands-on experience with MLOps platforms: MLflow, Azure Machine Learning, AWS SageMaker, Google Vertex AI
- Experience with version control and DevOps tools: Git, GitHub, GitLab, Azure DevOps
- Expertise in containerization and orchestration: Docker and Kubernetes
- Experience building CI/CD pipelines for machine learning applications
- Infrastructure as Code experience using Terraform, ARM Templates, Bicep, or equivalent
- Experience with cloud platforms: Microsoft Azure, AWS, GCP
- Experience with enterprise databases and data platforms: SQL Server, PostgreSQL, MySQL, Data Lakes, Data Warehouses, ETL/ELT pipelines
- Hands-on experience implementing monitoring and observability: Prometheus, Grafana, Azure Monitor, or cloud-native monitoring
- Strong understanding of IAM, cloud security, infrastructure security, and enterprise governance practices
- Experience deploying and supporting production-scale Machine Learning systems
- Strong analytical thinking, troubleshooting, problem-solving, communication, and stakeholder management skills
- Ability to collaborate across Data Science, AI Engineering, Cloud Infrastructure, and DevOps teams and strong ownership mindset
- Experience with Generative AI and Large Language Models (LLMs)
- Knowledge of Retrieval-Augmented Generation (RAG) and enterprise knowledge retrieval solutions
- Familiarity with foundation model platforms and APIs: OpenAI, Azure OpenAI, Claude, Gemini
- Familiarity with AI orchestration frameworks: LangChain, Semantic Kernel, AutoGen, CrewAI
- Experience with distributed data processing and orchestration: Apache Airflow, Prefect, Apache Spark, Databricks
- Knowledge of Vector Databases: Pinecone, Weaviate, FAISS, ChromaDB
- Exposure to Responsible AI, Explainable AI, AI Governance, and Model Explainability frameworks
- Experience in Manufacturing, Automotive, Healthcare, Financial Services, Supply Chain, or Enterprise AI domains
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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.
