Enterpret: LLMOps Architect

Job Description:

Enterpret is looking for an experienced LLMOps Architect to join its Bengaluru team and build the foundation for scalable, production-grade AI infrastructure. This role is ideal for professionals with deep expertise in MLOps, machine learning infrastructure, and large language model operations who want to shape the future of AI-native applications. Enterpret helps global companies like Notion, Canva, Figma, and Perplexity transform customer feedback into actionable insights using advanced AI systems. As an LLMOps Architect, you will play a critical role in optimizing how AI models are trained, deployed, monitored, and scaled across the organization.

In this role, you will lead the architecture and operationalization of machine learning systems across experimentation and production environments. You will work closely with OpenAI, Anthropic, AWS, and internal engineering teams to design resilient and cost-efficient ML infrastructure. The position involves building CI/CD pipelines for machine learning, implementing observability systems, optimizing cloud resource usage, and ensuring robust governance and security across AI workflows. You will also contribute to enabling faster experimentation cycles and improving the reliability of AI-powered products through automation, monitoring, and scalable deployment practices.

Joining Enterpret offers an opportunity to work at the cutting edge of Generative AI, LLM infrastructure, and production AI systems. You will collaborate with researchers, backend engineers, and product teams to solve challenging problems related to retrieval systems, reinforcement learning, vector databases, and adaptive AI models. The company values ownership, innovation, and technical excellence, making this role perfect for professionals who enjoy building foundational systems, mentoring teams, and driving large-scale AI operations in a fast-moving startup environment.

Roles & Responsibilities:

• Design, develop, and scale Enterpret’s machine learning infrastructure for training, serving, retraining, and deploying LLM and encoder models.
• Build and maintain ML-focused CI/CD pipelines with features such as automated testing, canary deployments, rollbacks, and model versioning.
• Deploy and manage model serving systems for both real-time inference workloads and large-scale batch processing pipelines.
• Implement monitoring and observability systems to track model performance, latency, uptime, and data drift in production environments.
• Lead incident management, root cause analysis, and postmortem processes for AI and ML systems to ensure high reliability and availability.
• Optimize cloud infrastructure usage and ML workflows to balance scalability, performance, and cost efficiency across production systems.
• Establish governance, security, and compliance standards, including IAM management, data access controls, and model auditability.
• Collaborate with ML researchers and product teams to productionize GenAI features, retrieval systems, and reinforcement learning workflows.
• Evaluate and integrate tools related to model registries, orchestration frameworks, feature stores, and experimentation platforms.
• Mentor engineers and researchers by promoting MLOps best practices and enabling self-service infrastructure and deployment workflows.

Requirements & Eligibility:

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, or a related technical field.
• Minimum 6+ years of experience in MLOps, ML infrastructure, or AI platform engineering within high-growth or product-focused organizations.
• Strong expertise in AWS services such as SageMaker, EC2, EKS, S3, and IAM for deploying and managing machine learning infrastructure.
• Hands-on experience with infrastructure-as-code tools like Terraform and container orchestration technologies including Docker and Kubernetes.
• Advanced Python programming skills with additional scripting knowledge in Go, Bash, or Rust considered an advantage.
• Experience building CI/CD pipelines using tools like GitHub Actions, Jenkins, or ArgoCD specifically for ML model deployment workflows.
• Strong understanding of model serving, observability, monitoring systems, and production incident management for AI applications.
• Familiarity with modern Generative AI ecosystems including LangChain, vector databases, retrieval-augmented generation (RAG), and LLM fine-tuning.
• Proven ability to optimize cloud costs, automate repetitive workflows, and improve deployment efficiency for large-scale AI systems.
• Excellent leadership, mentoring, and communication skills with the ability to collaborate across engineering, research, and product teams.

Expected Salary:

The expected salary for an LLMOps Architect at Enterpret in Bengaluru typically ranges between ₹40 LPA to ₹80 LPA depending on experience, technical depth, and expertise in AI infrastructure and MLOps systems. Professionals with strong experience in production LLM deployment, cloud optimization, and scalable ML platforms may receive even higher compensation packages along with equity benefits. Enterpret also offers premium healthcare, flexible work culture, and opportunities to work directly on advanced Generative AI systems.

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