3 days ago
Pune, IndiaSenior
Responsibilities
- Design and maintain end-to-end ML pipelines covering ingestion, preprocessing, training, evaluation, deployment, and monitoring.
- Build workflow orchestration and CI/CD automation for code quality, security scanning, testing, model validation, and deployments.
- Containerize and deploy AI/ML workloads on Kubernetes, serverless platforms, and cloud-native AI services using advanced deployment strategies.
- Implement monitoring, observability, dashboards, alerting, drift detection, incident response, and reliability practices for AI systems.
- Operate and optimize cloud AI workloads, infrastructure, networking, storage, security controls, managed AI services, and Infrastructure-as-Code.
- Build GenAI workflows, RAG architectures, embedding pipelines, vector database integrations, index refresh processes, and knowledge retrieval systems.
- Apply secure deployment, governance, privacy, RBAC, encryption, audit logging, and Responsible AI practices.
- Optimize GPU utilization, compute efficiency, model serving costs, token usage, and storage consumption.
- Collaborate with data scientists, AI engineers, platform engineers, and software teams to productionize models and integrate AI services.
- Create architecture diagrams, technical documentation, runbooks, SOPs, deployment guides, and on-call documentation.
Requirements
- 5+ years of experience in DevOps, Platform Engineering, SRE, or MLOps roles.
- 3+ years supporting machine learning, deep learning, or AI production systems.
- Proficiency with databases, especially graph databases such as Neo4j and Memgraph, plus SQL and NoSQL systems.
- Ability to perform data modeling and knowledge of embeddings, vector databases, and semantic search.
- Scripting and automation experience with Python, Golang, Bash, or similar languages.
- Strong hands-on expertise with Azure, AWS, or Google Cloud Platform and experience deploying AI/ML workloads at scale.
- Strong experience with Docker, Kubernetes, container orchestration, CI/CD pipelines, Infrastructure-as-Code, and monitoring and observability platforms.
- Working knowledge of LLMs, prompt engineering, RAG architectures, vector databases, and GenAI orchestration frameworks.
- Preferred experience with Azure OpenAI, Amazon Bedrock, Vertex AI, production LLM applications, GPU infrastructure, model evaluation, LLM observability, Responsible AI, governance, and security best practices.
- Relevant Azure, AWS, or GCP certifications are preferred.
Benefits
- Flexible working environment.
- Volunteer time off.
- LinkedIn Learning.
- Employee Assistance Program (EAP).
Tech Stack
Apache AirflowAWSAzureBashDatabricksDatadogDockerGitHub ActionsGoGoogle Cloud PlatformGrafanaJenkinsKubernetesNeo4jPrometheusPythonSQLTerraform