This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior AI ML Operations Engineer based in India.
This role offers the opportunity to build and scale reliable AI/ML operations platforms supporting enterprise-grade applications and data workloads.
You will bridge the gap between machine learning development and production through robust deployment, automation, observability, and infrastructure practices.
The position involves managing large-scale structured and unstructured data across modern cloud and lakehouse environments.
You will work with advanced AI/ML technologies, including Databricks, MLflow, LangChain, LangGraph, RAG, vector search, and knowledge graph capabilities.
A key focus will be improving platform reliability, scalability, security, performance, and cost efficiency across the AI lifecycle.
You will collaborate closely with data scientists, data engineers, and software engineers while driving modern engineering and AI-native practices.
This is a high-impact opportunity to shape production AI infrastructure in a fast-moving, innovation-focused environment.
Accountabilities
- Design, develop, and maintain stable, scalable, secure, and reliable AI/ML Operations platforms and production pipelines.
- Package, deploy, and manage AI/ML services and models in production environments, ensuring reproducibility, reliability, and interpretability.
- Design and implement automated CI/CD pipelines to streamline model deployment and operational workflows.
- Provision, configure, and optimize infrastructure for AI/ML training and inference using Docker, Kubernetes, serverless technologies, and infrastructure-as-code practices.
- Implement monitoring and observability for model performance, data drift, latency, reliability, and platform health, including the use of tools such as Monte Carlo where appropriate.
- Automate model retraining, data workflows, and operational processes to maintain model accuracy and platform efficiency over time.
- Manage deployment of foundation models, fine-tuning workflows, and Retrieval-Augmented Generation solutions involving vector databases and knowledge graphs.
- Support AI/ML workflows using Databricks, MLflow, Mosaic AI Agent Framework, Unity Catalog, and Vector Search.
- Optimize GPU and CPU resources to control cloud costs while maintaining high-performance, low-latency inference.
- Establish effective version control and governance for data, code, models, and AI/ML assets.
- Implement security and data-governance measures to protect sensitive information and support compliance requirements.
- Troubleshoot complex platform, data, infrastructure, and model-related issues to maintain operational stability.
- Evaluate emerging AI, data, and infrastructure technologies and identify opportunities to improve platform capabilities.
- Collaborate with data scientists, data engineers, and software engineers to transition models and AI solutions from development into reliable production environments.
Requirements
- Proven experience working with enterprise SaaS solutions requiring high availability, scalability, reliability, and performance.
- Strong experience building and maintaining AI/ML Operations platforms and production workflows at scale.
- In-depth experience handling large-scale structured and unstructured data, including environments involving petabytes of data.
- Hands-on expertise with the Databricks Lakehouse ecosystem, including AI/ML workflows, MLflow, Unity Catalog, Vector Search, and related technologies.
- Experience with the Mosaic AI Agent Framework, knowledge graphs, and modern AI/ML platform architectures.
- Strong knowledge of AI/ML frameworks such as LangChain and LangGraph, particularly for integrating AI capabilities into operational pipelines.
- Practical experience deploying and operating AI/ML workloads on at least one major cloud platform, such as AWS, Azure, or GCP; AWS-hosted data platforms are preferred.
- Strong programming skills in Python and SQL.
- Experience with modern software engineering and platform practices, including Kubernetes, Docker, CI/CD, infrastructure as code, and observability.
- Experience with infrastructure-as-code tools such as Terraform is preferred.
- Strong understanding of monitoring, alerting, reliability engineering, and production support for AI/ML platforms.
- Demonstrated ability to optimize cloud infrastructure and AI/ML workloads for cost and performance.
- Strong troubleshooting, analytical, and problem-solving abilities.
- Ability to collaborate effectively across data science, data engineering, software engineering, and other technical teams.
- Legally eligible to work in India on an ongoing basis.
Benefits
- Competitive compensation aligned with the scope and seniority of the role.
- Opportunity to work on large-scale AI/ML infrastructure and enterprise SaaS technologies.
- Exposure to advanced technologies across Databricks, Lakehouse architectures, MLflow, LangChain, LangGraph, RAG, vector search, and knowledge graphs.
- Opportunity to contribute to AI-driven products and modern AI-native engineering practices.
- Collaborative environment where ideas, experimentation, and technical innovation are encouraged.
- Support for professional growth and opportunities to expand expertise across AI, cloud, data, and platform engineering.
- Inclusive workplace committed to equal employment opportunities and diverse perspectives.
- Accessibility and interview accommodations available to support candidates throughout the hiring process.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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