Technical Lead- ML Development

Country/Region: IN

Requisition ID: 28351

Work Model:

Position Type:

Salary Range:

Location: INDIA - MUMBAI - CRISIL

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Description:

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• 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.
  

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.
  

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

• Computer Science, or
  

• Any Engineering discipline, or
  

• Mathematics or related quantitative fields.