Aziro - Senior Machine Learning Engineer - MLOps

Description :

Role : Senior Machine Learning Engineer with DevOps Expertise

Location : Bangalore

Experience : 5 to 10 yrs

Role Summary :

Mandatory Skills- Linux, Python, Kubernetes, Prometheus, Grafana, ELK stack, Tracing, DevOps, LangServe, LangGraph, LangFuse, Datadog, PagerDuty.

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.

Required 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.