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MLOPS/Machine Learning Operations Engineer

2 weeks ago

Mumbai, Maharashtra, India HR Solutions Full time ₹ 15,00,000 - ₹ 20,00,000 per year

Urgent Opening - MLOPS Engineer -Mumbai for one of the service Based MNC -Hybrid Mode

Apply Immediately on or call on on whats app CV or call to arrange the Interview .

What Youll 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 Were Looking For:

Experience & Skills:

  • 3 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:

  • Bachelors or Master's degree