MLOps Engineer (AWS / Azure / GCP)
Job Summary
Job Description
This MLOps Engineer role requires building complex ETL code for AI/ML pipelines, designing data engineering alerting/monitoring workflows, and implementing automation strategies for ML infrastructure management. Responsibilities include DevOps pipelines, scripting, Infrastructure as Code, building impactful customer relationships, sharing best practices, productionalizing data science models at scale, assisting data science teams in experimentation, and utilizing GCP services like Vertex AI or Kubeflow Pipelines. Experience with machine learning frameworks (TensorFlow, PyTorch), libraries (scikit-learn, HuggingFace), prompt engineering, LangChain, vector databases, and LLMOps is essential. The role demands expertise in CI/CD, IaC (Terraform), Docker, Kubernetes, SQL, Java, and Python, along with experience in the full ML lifecycle, model performance monitoring, and strong communication skills.
Must have:
- 5+ years ML Ops experience
- 2+ years building end-to-end data pipelines
- 2+ years deploying ML models in GCP
- 3+ years experience with SQL, Java, Python
- GCP expertise (Vertex AI/Kubeflow Pipelines)
- Experience with ML frameworks (TensorFlow, PyTorch)
Good to have:
- Knowledge of prompt engineering, LangChain, vector databases
- Experience with MLFlow or other MLOps tools
- Familiarity with model performance monitoring and data drift detection
15 skills required
Job Details
What you will be doing: Build complex ETL code to enable AI and ML pipelines Design and implement complex Data Engineering Alerting and monitoring workflows Design and execute automation strategies to wholistically manage ML infra, encompassing DevOps pipelines, scripting, Infrastructure as Code Establish credibility and build impactful relationships with our customers. Capture and share industry best practices amongst the MLOps community Productionalizing Data Science models to serve at scale Assist Data Science teams in experimentation
Exp - 4+yrs
Skill Required : MLOPS + Azure/AWS / GCP skillset.
Location - Bangalore
Requirements:
• Expertise in public cloud services, particularly in GCP.
• Experience working with VertexAI / Kubeflow Pipelines or other MLOps tools like MLFlow.
• Experience with machine learning frameworks (TensorFlow, PyTorch) and libraries (e.g., scikit-learn, HuggingFace).
• Knowledge of prompt engineering methods, LangChain, Vector databases and machine learning algorithms.
• Knowledge of LLMOps concepts and practices, including model versioning, deployment, monitoring, and efficient resource utilization for large language models.
• Experience in Infrastructure and Applied DevOps principles utilizing tools for continuous integration and continuous deployment (CI/CD), and Infrastructure as Code (IaC) like Terraform to automate and improve development and release processes.
• Knowledge of containerization technologies such as Docker and Kubernetes to enhance the scalability and efficiency of applications.
• Proven experience in engineering machine learning systems at scale.
• Strong programming abilities in SQL, Java and Python.
• Proven experience with the entire ML lifecycle (processing, training, evaluation, deployment, serving, monitoring).
• Familiarity with model performance monitoring, data drift detection, and anomaly detection (hands-on experience preferred).
• Strong understanding of IT infrastructure and operations, with the ability to set up monitoring/alerting tools and access controls for both infrastructure and applications.
• Have strong communication skills to collaborate effectively with cross-functional teams.
• Demonstrate a willingness to adapt to new technologies and methodologies in the ever-evolving AIOps landscape.
Must Have:
• Overall 5+ years experience in ML Ops.
• 2+ years of experience building end-to-end data pipelines.
• 2+ years of experience building and deploying ML models in a GCP.
• 3+ years of experience working with SQL, Java, Python for data analysis.
programming.
• Technical degree: Computer Science, software engineering or related
• Based in Bangalore and willing to work from the office 3 days a week
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