Role: LLMOps Engineer

Experience: 6 to 10 Years

Skills: LLMOps, GenAI, MLOps, Kubernetes, Docker, Python, Azure/AWS/GCP, CI/CD

Job Summary

- Deploy, manage, and scale LLM-powered applications across cloud and hybrid environments.
- Build and maintain production-grade inference pipelines for Generative AI workloads.
- Design and implement LLMOps pipelines for model, prompt, and configuration lifecycle management.
- Monitor latency, throughput, cost, errors, model drift, and hallucinations.
- Develop dashboards, logging frameworks, and alerting systems to ensure service reliability.
- Optimize inference performance and costs using caching, batching, routing, and autoscaling.
- Implement security, governance, access control, and audit mechanisms for AI systems.
- Collaborate with AI/ML, Responsible AI, and Application Engineering teams.
- Enable enterprise AI adoption through reusable LLMOps platforms and frameworks.

Required Skills

- Solid understanding of LLMs, Generative AI architectures, and inference workflows.




- Experience operating LLM/GenAI solutions in production environments.
- Hands-on expertise in Azure, AWS, or GCP.
- Strong experience with Docker, Kubernetes, and container orchestration.
- Experience with CI/CD pipelines for ML/AI applications.
- Proficiency in Python and automation scripting.
- Knowledge of MLOps/LLMOps tools, observability, monitoring, and logging solutions.
- Experience in performance tuning, scalability, and cost optimization of AI workloads.

Preferred Skills

- Experience with Vector Databases, RAG, LangChain/LlamaIndex.
- Exposure to AI governance, Responsible AI, and compliance frameworks.
- Familiarity with monitoring tools such as Prometheus, Grafana, OpenTelemetry, or equivalent.

Experience

- 6 to 10 years of overall experience in DevOps, Platform Engineering, MLOps, or AI Engineering.
- 2 to 4+ years of hands-on experience supporting ML/GenAI workloads in production.

📌 LLMOps Engineer (Uttar Pradesh)
🏢 HCLTech
📍 Uttar Pradesh