MLOps Engineer

Requirement : MLOps EngineerTechnology : MLOps, LLM, Machine Learning, Docker, KubernetesExperience : 5+ yearsWork Location : Western Suburb, MumbaiClient Location : Navi MumbaiCANDIDATES FROM / IN Western Suburb /MUMBAI LOCATION SHALL ONLY APPLY. This role is open exclusively to candidates based in Western Suburb/ MUMBAI. Applications from candidates requiring RELOCATION WILL NOT BE ENTERTAINED.Notice Period : Immediate to 15 days ONLY (Urgent Requirement)Job Description :- 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.Desired Profile :- 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.Qualification : Bachelors or Masters degree from premier Indian institutes : - (IITs, IISc, NITs, BITS, IIITs etc.) in : Computer Science, or Any Engineering discipline, or Mathematics or related quantitative fields. (ref:hirist.tech)