machine learning

For over 20 years, Smartsheet has empowered teams to manage work seamlessly and scale solutions smarter. Now, in our most ambitious chapter yet, we are uniting human teams with AI agents. By orchestrating the work agents do best, automating manual tasks and uncovering insights at scale, we create the space for people to focus on what truly matters: judgment, creativity, and big thinking. That is magic at work, and it’s what we show up for every day.

Our India Global Capability Center isn't just supporting global operations—we’re leading global innovation. After scaling rapidly into a best-in-class hub, we deliver the product innovation and enterprise capabilities that accelerate our global growth, profitability, and scale. As we expand Smartsheet India, we’re searching for Senior AI/ML Ops Engineers who crave variety and ownership. You’ll have the opportunity to work across multiple teams and disciplines, building a versatile skillset while solving the complex challenges of a global platform.

You Will:

• Designing, Developing and overseeing the strategy and architecture of scalable and reliable AI/ML Ops platforms / pipelines
• Model Deployment: Package and deploy AI/ML services to production, ensuring they are reproducible and interpretable
• CI/CD Pipeline Development: Design and implement automated CI/CD (Continuous Integration/Continuous Deployment) pipelines to accelerate model deployment using tools
• Infrastructure Management: Provision and optimize infrastructure for training and serving, utilizing Docker, Kubernetes, or serverless platforms
• Monitoring & Observability : Implement post-deployment monitoring for model performance, data drift, and latency using tools. Experience in Monte Carlo is preferable
• Automation: Automate retraining and data pipeline workflows to ensure models stay accurate over time.
• Manage the deployment of foundation models, fine-tuning workflows, and Retrieval-Augmented Generation (RAG) stacks (Vector DBs, Knowledge Graph. Experience with AWS Bedrock is preferable
• Resource Optimization: Manage GPU/CPU utilization to minimize cloud costs while maintaining low-latency inference for users
• Collaboration: Work closely with data scientists, data engineers, and software engineers to bridge the gap between model development and production.
• Version Control & Governance: Manage versioning for data, code, and models using tools like MLflow.
• Security & Compliance: Implementing data security measures, ensuring compliance with data governance policies, and protecting sensitive data
• Technology Evaluation and Innovation: Staying abreast of emerging data technologies and exploring opportunities for innovation to improve the organisation’s data infrastructure
• Troubleshooting and Problem Solving: Diagnosing and resolving complex data-related issues, ensuring the stability and reliability of the data platform
• Perform other duties as assigned

You Have:

• Enterprise SaaS software solutions with high availability and scalability
• Solution handling large scale structured and unstructured data from varied data sources
• Experience in building and maintaining AI/ML Ops platform systems ensuring scalability, reliability, efficiency and security
• Working with Product engineering team to influence designs with data, AI and analytics use cases in mind
• In depth experience in System design, AI/ML Frameworks and tools involving large Petabytes of data with Databricks Lakehouse ecosystem
• AI/MLOps workflows on Databricks , MLFlow, Mosaic AI Agent Framework, Unity Catalog, Vector Search, Knowledge Graph
• Knowledge of AI/ML frameworks like LangChain, LangGraph for AI/ML Ops pipeline integration
• Cloud Platforms: Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP). Experience in AWS hosted data platform is preferable
• Programming languages like Python and SQL
• Modern software engineering practices like Kubernetes, CI/CD, IAC tools (Preferably Terraform), Observability, monitoring and alerting
• Solution Cost Optimisations and design to cost
• Legally eligible to work in India on an ongoing basis

Get to Know Us:

At Smartsheet, your ideas are heard, your potential is supported, and your contributions have real impact. You’ll have the freedom to explore, push boundaries, and grow beyond your role. We welcome diverse perspectives and nontraditional paths—because we know that impact comes from individuals who care deeply and challenge thoughtfully. When you’re doing work that stretches you, excites you, and connects you to something bigger, that’s magic at work. Let’s build what’s next, together.

Equal Opportunity Employer:

Smartsheet is an Equal Opportunity (EEO) employer committed to fostering an inclusive environment with the best employees. It is our policy to provide equal employment opportunities to all qualified applicants in accordance with applicable laws in the US, UK, Australia, Germany, Costa Rica, Japan, Bulgaria, India, and Singapore. All qualified applicants will receive consideration without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, protected veteran or disabled status, or genetic information. 

If there are preparations we can make to help ensure you have a comfortable and positive interview experience, please let us know.

Job application link : https://grnh.se/z7qx2ehx1us

Job Title : MLOps Engineer

Mode: Hybrid

Experience : 4 to 7 Years

Location : Hyderabad (Priority)/Bengaluru locations only

Notice Period : Immediate Joiner

Job Summary:

We are looking for a skilled and proactive ML Engineer with strong expertise in Python, Databricks, and Machine Learning model development. The ideal candidate should be proficient in building scalable data pipelines and deploying ML models, with a working knowledge of MLOps principles and tooling. This role offers an opportunity to work on impactful AI/ML initiatives in a collaborative environment.

Key Responsibilities:

• Develop and maintain machine learning pipelines for training, testing, and deploying models

• Design and implement infrastructure for managing and monitoring machine learning models

• Work with data scientists to build scalable, efficient, and automated model training and testing processes

• Collaborate with software engineers to integrate machine learning models into production systems

• Automate and optimize the deployment and scaling of machine learning models in a distributed computing environment

• Monitor and troubleshoot machine learning systems and infrastructure to ensure high availability and performance

• Develop and maintain documentation and best practices for MLOps processes and procedures.

Experience:

Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field

• 3+ years of experience in MLOps or related field, including building and deploying machine learning models at scale

•Proficiency in programming languages such as Python, Java, and C++

•Experience with machine learning frameworks such as TensorFlow, PyTorch, and Keras

• Experience with containerization technologies such as Docker and Kubernetes

• Strong understanding of DevOps principles and practices

• Experience with cloud computing platforms such as AWS, Azure, or Google Cloud

Experience: 5+ Years

Employment Type: Full-Time

Role Overview

We are looking for an experienced GCP Data Engineer with 5+ years of experience in data engineering and strong hands-on expertise in Google BigQuery, Google Cloud Storage (GCS), Airflow/Cloud Composer, Python, and Vertex AI. The candidate should be capable of designing, developing, and maintaining scalable data pipelines and cloud-based data solutions on Google Cloud Platform.

Key Skills – Mandatory

• BigQuery – Strong hands-on experience in data warehousing, SQL, optimization, and performance tuning.
• Google Cloud Storage (GCS) – Experience with data storage, file management, and integration with data pipelines.
• Airflow / Cloud Composer – Experience in developing, scheduling, monitoring, and managing data workflows.
• Python – Strong programming skills for data engineering, ETL/ELT development, automation, and pipeline implementation.
• Vertex AI – Experience working with ML/AI workflows, model integration, or data pipelines supporting AI/ML solutions.

Good to Have / Added Advantage

• Dataproc – Experience with distributed data processing and Spark-based workloads.
• Cloud Data Fusion – Experience in building and managing data integration pipelines.
• Cloud Run – Understanding of deploying and running containerized applications/services on GCP.
• Experience with ETL/ELT processes and data pipeline development.
• Knowledge of GCP data architecture and cloud-native services.
• Experience in data quality, validation, monitoring, and troubleshooting.

Responsibilities

• Design, develop, and maintain scalable GCP-based data pipelines.
• Build and optimize data solutions using BigQuery and Cloud Storage.
• Develop and manage workflows using Airflow / Cloud Composer.
• Write efficient and reusable Python code for data processing and automation.
• Support Vertex AI integrations and AI/ML data workflows.
• Monitor pipeline performance and troubleshoot data processing issues.
• Work with cross-functional teams to understand data requirements and deliver reliable solutions.
• Implement best practices for data security, quality, scalability, and performance.

You must have :

• 5+ years of overall experience in Data Engineering.
• Strong hands-on experience with BigQuery, GCS, Airflow/Cloud Composer, Python, and Vertex AI.
• Strong understanding of data engineering concepts, ETL/ELT, data pipelines, and cloud technologies.
• Dataproc, Data Fusion, and Cloud Run experience will be an added advantage.

Hiring AI ML Engineer

Exp : 8 - 11 yrs

Edu : BE/B.Tech/MCA

Work Location : Pune

Skills :

Experience with MLOps processes and tools.

Experience with SQL, Databricks, MLFlow and PowerBI/Tableau.

Hands-on experience with MLOps tools and cloud ML platforms such as MLflow, Databricks, Azure ML, or equivalent.

Strong SQL and data analysis skills for validation, troubleshooting, monitoring, and reporting.

Experience with model lifecycle management, including model registry, versioning, lineage, reproducibility, deployment tracking, and monitoring.

Familiarity with dashboards and alerting tools such as Power BI, Tableau, Databricks SQL dashboards, or equivalent.

Working knowledge of Git, CI/CD, scripting, and production support practices would be advantageous.

We are seeking a highly skilled and experienced DevOps Engineer to join our development team.

Years of experience needed: 8+ Yrs

Required Skills

Strong experience with GitLab, TeamCity, Terraform, Kubernetes and Docker

Proficiency in various deployment strategies and CI/CD pipeline setups

Solid understanding of Linux administration and Windows IIS.

Advanced scripting skills in bash and PowerShell

Hands-on experience with Google Cloud Platform (GCP) services, particularly GKE, GCS, GCE, Cloud SQL, and load balancers

Familiarity with Autosys for job scheduling and automation

Excellent problem-solving and troubleshooting skills

Ability to work independently and collaboratively in a fast-paced environment

• • Strong communication and interpersonal skills.

About the Role

We are looking for a highly skilled Data Scientist with strong expertise in Machine Learning, MLOps, and Generative AI. The ideal candidate will have hands-on experience in building scalable ML models, deploying them in production, and working with modern AI frameworks, including GenAI technologies.

Key Responsibilities

·      Design, develop, and deploy machine learning models for real-world business problems

·      Work on end-to-end ML lifecycle: data preprocessing, model building, evaluation, deployment, and monitoring

·      Implement and manage MLOps pipelines for scalable and reproducible workflows

·      Utilize tools like MLflow for experiment tracking, model versioning, and lifecycle management

·      Develop and integrate Generative AI (GenAI) solutions such as LLM-based applications

·      Collaborate with cross-functional teams (engineering, product, business) to translate requirements into AI solutions

·      Optimize model performance and ensure production stability

·      Stay updated with the latest advancements in AI/ML and GenAI ecosystems

Required Skills & Qualifications

·      4+ years of experience in Data Science / Machine Learning

·      Strong programming skills in Python

·      Hands-on experience with ML modeling techniques (supervised, unsupervised, NLP, etc.)

·      Solid understanding of MLOps practices and tools

·      Experience with MLflow or similar model lifecycle tools 

·      Practical experience in Generative AI (GenAI), including working with LLMs

·      Experience with libraries/frameworks like Scikit-learn, TensorFlow, PyTorch

·      Strong understanding of data structures, algorithms, and statistics

·      Experience with cloud platforms (AWS/GCP/Azure) is a plus

Good to Have

·      Experience with LLM fine-tuning, prompt engineering, or RAG pipelines

·      Exposure to Docker, Kubernetes, and CI/CD pipelines

·      Knowledge of data engineering workflows 

Description

We’re seeking a highly skilled, execution-focused Senior Data Scientist with a minimum of 5 years of experience. This role demands hands-on expertise in building, deploying, and optimizing machine learning models at scale, while working with big data technologies and modern cloud platforms. You will be responsible for driving data-driven solutions from experimentation to production, leveraging advanced tools and frameworks across Python, SQL, Spark, and AWS. The role requires strong technical depth, problem-solving ability, and ownership in delivering business impact through data science.

Responsibilities

• Design, build, and deploy scalable machine learning models into production systems.
• Develop advanced analytics and predictive models using Python, SQL, and popular ML/DL frameworks (Pandas, Scikit-learn, TensorFlow, PyTorch).
• Leverage Databricks, Apache Spark, and Hadoop for large-scale data processing and model training.
• Implement workflows and pipelines using Airflow and AWS EMR for automation and orchestration.
• Collaborate with engineering teams to integrate models into cloud-based applications on AWS.
• Optimize query performance, storage usage, and data pipelines for efficiency.
• Conduct end-to-end experiments, including data preprocessing, feature engineering, model training, validation, and deployment.
• Drive initiatives independently with high ownership and accountability.
• Stay up to date with industry best practices in machine learning, big data, and cloud-native deployments.

Requirements

• Minimum 5 years of experience in Data Science or Applied Machine Learning.
• Strong proficiency in Python, SQL, and ML libraries (Pandas, Scikit-learn, TensorFlow, PyTorch).
• Proven expertise in deploying ML models into production systems.
• Experience with big data platforms (Hadoop, Spark) and distributed data processing.
• Hands-on experience with Databricks, Airflow, and AWS EMR.
• Strong knowledge of AWS cloud services (S3, Lambda, SageMaker, EC2, etc.).
• Solid understanding of query optimization, storage systems, and data pipelines.
• Excellent problem-solving skills, with the ability to design scalable solutions.
• Strong communication and collaboration skills to work in cross-functional teams.

Benefits

• Best-in-class salary: We hire strong talent and compensate accordingly.
• Proximity Talks: Meet and learn from designers, engineers, product leaders, and AI practitioners.
• Continuous learning: Work with a world-class team and stay close to the latest in AI, engineering, and product development.
• High-impact work: Build AI-first systems and products used at scale by global clients.

About Us

Proximity is the trusted technology, design, and consulting partner for some of the biggest Sports, Media, and Entertainment companies in the world. We’re headquartered in San Francisco and have offices in Palo Alto, Dubai, Mumbai, and Bangalore.

Since 2019, Proximity has built high-impact, scalable products used by millions of users every day. Today, we are a global team of engineers, designers, product managers, and experts solving complex problems and building cutting-edge technology at scale.

Job Description:

• Infrastructure Management: Design, implement, and manage scalable, reliable, and secure cloud infrastructure using AWS, GCP, and/or Azure.
• CI/CD Pipelines: Develop and maintain continuous integration and continuous deployment (CI/CD) pipelines to streamline the development lifecycle.
• Automation: Automate infrastructure provisioning, configuration management, and application deployment processes.
• Monitoring and Performance: Implement monitoring, logging, and alerting solutions to ensure system health, performance, and reliability.
• Security: Ensure the security of cloud infrastructure and applications, including identity management and compliance with industry standards.
• Collaboration: Work closely with client and development teams to integrate DevOps practices and deliver high-quality software.
• Documentation: Maintain comprehensive documentation of infrastructure, configurations, and processes.
• Innovation: Stay current with emerging technologies and industry trends, integrating them into the DevOps strategy as appropriate.

Qualifications: 

• Education: Bachelor's degree in Computer Science, Information Technology, or a related field.
• Experience: 7 - 10 years of overall experience with relevant experience of at least 7 years in DevOps and served as a lead or senior engineer.

Job Summary

We are seeking a highly skilled GCP Data Engineer with strong expertise in Google Cloud Platform (GCP), Python, ETL, and modern data engineering technologies. The ideal candidate should have hands-on experience designing and building scalable data pipelines using BigQuery, Dataflow, Pub/Sub, Airflow, and modern data lake technologies such as Apache Iceberg or Delta Lake.

Key Responsibilities

• Design, develop, and maintain scalable ETL/ELT data pipelines on Google Cloud Platform.
• Build and optimize data processing workflows using Python and Google Cloud Dataflow (Apache Beam).
• Develop and manage large-scale analytical data models in BigQuery.
• Implement event-driven data ingestion using Google Cloud Pub/Sub.
• Create, schedule, and monitor workflows using Apache Airflow and Autosys.
• Design and implement modern data lake architectures using Apache Iceberg or Delta Lake.
• Optimize query performance, storage, and compute costs in GCP.
• Ensure data quality, governance, security, and compliance across data platforms.
• Collaborate with Data Scientists, Analysts, and Application teams to deliver scalable data solutions.
• Troubleshoot production issues and continuously improve pipeline reliability and performance.

Mandatory Skills

• Strong hands-on experience with Google Cloud Platform (GCP).
• Proficiency in Python programming.
• Experience in designing and implementing ETL/ELT pipelines.
• Strong knowledge of BigQuery.
• Experience with Google Cloud Dataflow (Apache Beam).
• Experience with Google Cloud Pub/Sub.
• Hands-on experience with Apache Airflow.
• Experience in job scheduling using Autosys.
• Experience with modern table formats such as Apache Iceberg or Delta Lake.
• Strong SQL and data modeling skills.

Preferred Skills

• Experience with Cloud Storage, Dataproc, Cloud Composer, and Cloud Functions.
• Knowledge of CI/CD pipelines and DevOps practices.
• Experience with Docker and Kubernetes.
• Familiarity with Git and Agile/Scrum methodologies.
• Knowledge of data warehousing and dimensional modeling.
• Exposure to streaming and real-time data processing.

Qualifications

• Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, or a related field.
• 4–8+ years of experience in Data Engineering with hands-on expertise in GCP technologies.

Required Experience

• Strong experience in developing enterprise-grade data pipelines using Python and GCP.
• Hands-on experience with BigQuery, Dataflow, Pub/Sub, and Airflow.
• Experience scheduling and monitoring batch workflows using Autosys.
• Experience implementing modern data lake architectures using Apache Iceberg or Delta Lake.
• Strong understanding of ETL best practices, performance tuning, and data optimization.
• Excellent analytical, troubleshooting, and problem-solving skills.

Mandatory Skills

• Google Cloud Platform (GCP)
• Python
• ETL
• BigQuery
• Autosys
• Apache Airflow
• Google Cloud Pub/Sub
• Google Cloud Dataflow (Apache Beam)
• Apache Iceberg / Delta Lake
• SQL & Data Modeling

Role Overview

We are looking for a GCP Data Engineer with 10+ years of experience to design, develop, and optimize scalable cloud-based data solutions. The ideal candidate will have strong hands-on expertise in GCP, BigQuery, and advanced SQL, with experience building data pipelines and working with large-scale datasets.

Key Responsibilities

• Design and develop scalable data pipelines and ETL/ELT processes on GCP.
• Build, optimize, and maintain data solutions using Google BigQuery.
• Develop complex SQL queries for data transformation, aggregation, and analysis.
• Design efficient data models and optimize pipelines for performance, scalability, and cost.
• Integrate data from multiple sources and ensure data quality, reliability, and availability.
• Troubleshoot pipeline and data issues and drive continuous improvement.
• Collaborate with data architects, analysts, application teams, and business stakeholders.
• Follow best practices for cloud security, data governance, testing, and documentation.

Required Skills

• 8+ years of Data Engineering experience
• Strong hands-on experience with GCP, Django, and MongoDB
• Extensive experience with BigQuery
• Advanced SQL skills
• Strong understanding of ETL/ELT and data pipeline development
• Data modeling and data warehousing experience
• Experience handling large-scale datasets and performance optimization
• Strong problem-solving and communication skills

Good to Have

• GCP services such as Cloud Storage, Dataflow, Pub/Sub, Cloud Composer, or Cloud Functions
• Python or other data engineering languages
• Experience with data governance and security
• Agile development experience