What You'll Do :

- Develop, and manage efficient MLOps pipelines tailored for Large Language Models, automating the deployment and lifecycle management of models in production.

- Deploy, scale, and monitor LLM inference services across cloud-native environments using - Kubernetes, Docker, and other container orchestration frameworks.

- Optimize LLM serving infrastructure for latency, throughput, and cost, including hardware acceleration setups with GPUs or TPUs.

- Build and maintain CI/CD pipelines specifically for ML workflows, enabling automated validation, and seamless rollouts of continuously updated language models.

- Implement comprehensive monitoring, logging, and alerting systems (e.g., Prometheus, Grafana, ELK stack) to track model performance, resource utilization, and system health.

- Collaborate cross-functionally with ML research and data science teams to operationalize fine-tuned models, prompt engineering experiments, and multi agentic LLM workflows.

- Handle integration of LLMs with APIs and downstream applications, ensuring reliability, security, and compliance with data governance standards.

- Evaluate, select, and incorporate the latest model-serving frameworks and tooling (e.g., Hugging Face Inference API, NVIDIA Triton Inference Server).

- Troubleshoot complex operational issues impacting model availability and degradation, implementing fixes and preventive measures.

- Stay up to date with emerging trends in LLM deployment, optimization techniques such as quantization and distillation, and evolving MLOps best practices.

What We're Looking For :

Experience & Skills :

- 3 to 5 years of professional experience in Machine Learning Operations or ML Infrastructure engineering, including experience deploying and managing large-scale ML models.

- Proven expertise in containerization and orchestration technologies such as Docker and Kubernetes, with a track record of deploying ML/LLM models in production.

- Strong proficiency in programming with Python and scripting languages such as Bash for workflow automation.

- Hands-on experience with cloud platforms (AWS, Google Cloud Platform, Azure), including compute resources (EC2, GKE, Kubernetes Engine), storage, and ML services.

- Solid understanding of serving models using frameworks like Hugging Face Transformers or OpenAI APIs.

- Experience building and maintaining CI/CD pipelines tuned to ML lifecycle workflows (evaluation, deployment).

- Familiarity with performance optimization techniques such as batching, quantization, and mixed-precision inference specifically for large-scale transformer models.

- Expertise in monitoring and logging technologies (Prometheus, Grafana, ELK Stack, Fluentd) to ensure production-grade observability.

- Knowledge of GPU/TPU infrastructure setup, scheduling, and cost-optimization strategies.

- Strong problem-solving skills with the ability to troubleshoot infrastructure and deployment issues swiftly and efficiently.

- Effective communication and collaboration skills to work with cross-functional teams in a fast-paced environment.

Educational Background :

- Bachelor's or Master's degree from premier Indian institutes (IITs, IISc, NITs, BITS, IIITs etc.) in :

1. Computer Science, or

2. Any Engineering discipline, or

3. Mathematics or related quantitative fields.