MLOps Engineer

6 hours ago


Bengaluru, Karnataka, India Aziro Full time ₹ 12,00,000 - ₹ 36,00,000 per year

Aziro Technologies (formerly MSys Technologies) is an AI-native product engineering company driving innovation-led tech transformation globally

We are hiring MLOps Engineers to join us immediately

Exp - 5+ years

Location - Bangalore (Hybrid Offer)

Notice Period - Immediate to 15 days

Job Description

About the Role

We are seeking a highly motivated Developer with strong expertise in Python, Generative AI platforms, and MLOps tools. The ideal candidate will have hands-on experience in Langserve, Langfuse, Docker, Kubernetes, and Groovy, with the ability to design, deploy, and scale AI-driven applications. This role combines software engineering, AI solutioning, and operational excellence.

Key Responsibilities

Develop, deploy, and manage AI/ML applications using Python and modern MLOps frameworks.

Implement, monitor, and scale Generative AI solutions on platforms such as Agentforce, N8, Microsoft Copilot Studio, or equivalent.

Utilize Langserve and Langfuse for building, serving, and monitoring AI pipelines.

Containerize and orchestrate applications using Docker and Kubernetes.

Write and maintain automation scripts using Groovy for CI/CD pipelines.

Collaborate with data scientists, solution architects, and product teams to deliver production-ready solutions.

Apply best practices for model deployment, observability, and lifecycle management.

Manage code repositories, workflows, and deployments via GitHub Actions and CI/CD pipelines.

Mandatory Skills

Strong experience in Python development (APIs, automation, data processing).

Hands-on expertise in MLOps frameworks: Langserve & Langfuse.

Proficiency with Docker for containerization.

Experience with Kubernetes for orchestration and scaling AI workloads.

Scripting expertise in Groovy (especially for CI/CD automation).

Practical understanding of GitHub workflows, CI/CD, and cloud integrations.

Good-to-Have Skills

Exposure to Generative AI frameworks (LangChain, LlamaIndex, RAG pipelines).

Knowledge of cloud environments (AWS, Azure, or GCP).

Familiarity with observability and monitoring tools (Prometheus, Grafana, ELK stack).

Experience in secure deployment and compliance practices for AI systems.


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