Job Description :

- Design, build, and maintain balanced Data, ML, and AI engineering pipelines to automate data provisioning, model deployment, and enterprise workflow execution.

- Implement and support robust LLMOps and MLOps practices across data, machine learning, and Generative AI systems to automate model evaluation, monitoring, and CI/CD workflows.

- Manage, optimize, and scale Databricks workspace configurations, clusters, and jobs for enterprise data processing and AI/ML workloads.

- Collaborate cross-functionally with data engineers, ML engineers, software engineers, and product leads to design, deploy, and scale data pipelines, feature stores, and ML serving systems into production.

- Implement proactive incident response, event instrumentation, and self-healing mechanisms to detect and remediate system anomalies or data/model quality issues.

- Provide day-to-day operational support, infrastructure upgrades, capacity planning, and cloud resource optimization for data platforms and ML infrastructure.

- Work closely with IT DevOps, SRE, and security teams to enforce governance, data lineage, compliance, and enterprise CI/CD deployment standards.

- Promote engineering best practices, conduct code reviews, participate in on-call rotation support, and contribute to knowledge sharing across teams.

- Develop hands-on Proofs of Concept (POCs) for modern data platforms, feature stores, and real-time streaming tools in collaboration with product and analytics teams.

- Maintain agility towards evolving technology stacks across DataOps and MLOps platforms to continually modernize infrastructure.

Experience Range : 2 - 5 years

Educational Qualifications :

- Bachelor's Degree in Computer Science

- Bachelor's Degree in Software Engineering

- Bachelor's Degree in related technical discipline

Job Responsibilities :

- Design, build, and maintain balanced Data, ML, and AI engineering pipelines to automate data provisioning, model deployment, and enterprise workflow execution.

- Implement and support robust LLMOps and MLOps practices across data, machine learning, and Generative AI systems to automate model evaluation, monitoring, and CI/CD workflows.

- Manage, optimize, and scale Databricks workspace configurations, clusters, and jobs for enterprise data processing and AI/ML workloads.

- Collaborate cross-functionally with data engineers, ML engineers, software engineers, and product leads to design, deploy, and scale data pipelines, feature stores, and ML serving systems into production.

- Implement proactive incident response, event instrumentation, and self-healing mechanisms to detect and remediate system anomalies or data/model quality issues.

- Provide day-to-day operational support, infrastructure upgrades, capacity planning, and cloud resource optimization for data platforms and ML infrastructure.

- Work closely with IT DevOps, SRE, and security teams to enforce governance, data lineage, compliance, and enterprise CI/CD deployment standards.

- Promote engineering best practices, conduct code reviews, participate in on-call rotation support, and contribute to knowledge sharing across teams.

- Develop hands-on Proofs of Concept (POCs) for modern data platforms, feature stores, and real-time streaming tools in collaboration with product and analytics teams.

- Maintain agility towards evolving technology stacks across DataOps and MLOps platforms to continually modernise infrastructure.

Skills Required :

Data Engineering, MLOps, AI Engineering, Data Provisioning, Model Deployment, Workflow Automation, LLMOps, Model Evaluation, Model Monitoring, CI/CD, Databricks, Data Pipelines, Feature Stores, ML Serving Systems, Incident Response, Event Instrumentation, Self-healing Mechanisms, Operational Support, Infrastructure Upgrades, Capacity Planning, Cloud Resource Optimization, DevOps, SRE, Security, Governance, Data Lineage, Compliance, Code Reviews, On-call Support, Proof of Concept Development, Real-time Streaming, DataOps, Problem Solving, Cross-functional Communication, MLflow, Weights & Biases, LangSmith, AWS, SageMaker, Glue, EMR, Athena, S3, Database Fundamentals, Replication, Relational Databases, NoSQL Databases, Vector Databases, Pinecone, FAISS, Milvus, Weaviate, Python, Java, Scala, Microservices, Git, ETL, Terraform, CloudFormation, Ansible, Bash, JavaScript, Docker, Kubernetes, Machine Learning Lifecycle, Deep Learning Frameworks, PyTorch, TensorFlow, NLP, CV, Feature Engineering

Candidate Attributes :

- Strong problem-solving mindset

- Exceptional cross-functional communication

- A track record of driving collaborative DevOps/DataOps/MLOps culture

- Solid understanding of modern DevOps, MLOps, and DataOps methodologies, including CI/CD automation, model governance, and observability tools (e.g., MLflow, Weights & Biases, LangSmith)

- Hands-on experience with core cloud data & ML services on AWS (e.g., SageMaker, Glue, EMR, Athena, S3)

- Strong expertise in Databricks management, optimisation, and workspace administration

- Strong understanding of database fundamentals, replication, relational/NoSQL databases, and vector databases (e.g., Pinecone, FAISS, Milvus, Weaviate)

- Proven experience building CI/CD automation pipelines for containerised Python, Java, or Scala microservices and ML serving systems

- Proficient with Git version control and standard branching workflows

- Practical experience deploying, monitoring, and debugging distributed data pipelines, ETL workflows, and model deployment systems

- Familiarity with configuration management and provisioning tools (e.g., Terraform, CloudFormation, Ansible)

- Scripting proficiency in one or more languages (Python, Bash, or JavaScript)

- Practical experience with containerization using Docker and exposure to Kubernetes orchestration

- Solid grasp of machine learning lifecycle, deep learning frameworks (PyTorch, TensorFlow), NLP/CV concepts, and feature engineering workflows

- 2 - 5 years of experience across DataOps, MLOps, ML Engineering, or Data Engineering in enterprise cloud environments