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MLOps Engineer

MLOps Engineer

Bolt-on Global Solutions Pvt Ltd.Mumbai
30+ days ago
Job description

Requirement : MLOps Engineer

Technology : MLOps, LLM, Machine Learning, Docker, Kubernetes

Experience : 5+ years

Work Location : Western Suburb, Mumbai

Client Location : Navi Mumbai

CANDIDATES 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)

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