GenAI MLOps Engineer
- Nielseniq India
- 4 - 6 years
- Pune
- 3 days ago
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Job Description
About the Role
As a GenAI MLOps Engineer on our AI Engineering team, youll build, deploy, and maintain the core infrastructure that powers our generative-AI products. Youll partner closely with data scientists and software engineers to productionize LLM-based models, automate workflows, and keep services reliable and cost-effective.
Core Responsibilities (Must-Have)
Pipeline & CI/CD
- Design, build, and operate repeatable ML pipelines (data prep training evaluation deploy) using tools such as Airflow, Prefect, or cloud-native solutions.
- Author automated CI/CD workflows (GitHub Actions, Azure DevOps, or Jenkins) for model code, pipelines, and container builds, including linting and automated tests.
- Model Deployment & Serving
- Containerize models with Docker; deploy to Kubernetes (AKS/EKS/GKE) or serverless (Cloud Run, Azure Functions).
- Implement safe rollout patterns (canary, blue/green) to minimize risk when updating model versions.
- Monitoring & Alerting
- Instrument inference endpoints and pipelines with key metrics (latency, throughput) and logs.
- Create dashboards and alerts (Prometheus/Grafana or cloud-native alternatives) to detect errors, drift, and performance regressions.
- Cloud & Infrastructure
- Operate core compute resources on one major cloud platform (Azure Databricks, AWS SageMaker, or GCP Vertex AI).
- Write and maintain basic Infrastructure-as-Code (Terraform, or CloudFormation) for provisioning clusters and managed services.
- GenAI Orchestration & Vector Retrieval
- Use orchestration framework (e.g., LangGraph, Langfuse etc) to automate GenAI workflows.
- Support embedding-based retrieval pipelines: collaborate on vector index maintenance and refresh processes.
- Collaboration & Documentation
- Work with data science to integrate new models into production.
- Produce clear runbooks, architecture diagrams, and on-call guides.
Qualifications
- 5 years in DevOps/MLOps roles, including at least 3 years supporting ML or deep-learning systems.
- Hands-on with one major cloud (Azure/AWS/GCP) and experience provisioning compute for training/inference.
- Strong skills in Docker and Kubernetes or serverless deployments.
- Proven ability to author CI/CD pipelines and IaC.
- Experience with monitoring stacks (Prometheus/Grafana, Datadog, or cloud-native tools).
- Familiarity with a prompt-orchestration framework (e.g., LangChain) and core vector-retrieval concepts.
Soft Skills
- Effective communicator who can translate technical details to cross-functional teams.
- Strong problem-solver who can troubleshoot across data, model, and infrastructure layers.
- Eager to learn new tools and iterate rapidly in a fast-paced environment.
Additional Information
Growth & Nice-to-Have
- Security & Compliance: Basic API auth (OAuth/JWT), secrets management (Key Vault, AWS KMS), and data encryption.
- Testing & Validation: Data-quality checks (e.g., Great Expectations), adversarial testing, and automated model-quality gates.
- Scalability & Cost Optimization: Capacity planning, load testing (Locust, JMeter), spot-instance usage, and caching strategies (Redis).
- Reliability Engineering: Participation in on-call rotations, post-mortems, and error-budget SLOs; chaos-testing fundamentals.
- Experimentation Lifecycle: Tracking experiments and hyperparameter sweeps (MLflow, Optuna), and supporting A/B tests.
- Tooling Flexibility: Familiarity with alternative MLOps frameworks (Kubeflow, TFX) or observability stacks (OpenTelemetry).
- Our Benefits
- Flexible working environment
- Volunteer time off
- LinkedIn Learning
- Employee-Assistance-Program (EAP)
Job Classification
Industry: Software ProductFunctional Area / Department: Data Science & AnalyticsRole Category: Data Science & Machine LearningRole: Machine Learning EngineerEmployement Type: Full timeContact Details:
Company: Nielseniq IndiaLocation(s): Pune
Keyskills: Docker Lang Chain Microsoft Azure Machine Learning Kubernetes Gen AI Aiml Devops ML
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