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LLM Systems & MLOps Architect

0 - 6 years

0 Lacs

delhi delhi

Posted:1 day ago| Platform: Indeed logo

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Skills Required

drive architecture optimization deployment ai learning engineering design ml training model pipeline tracking versioning microservices inference management mlflow sagemaker vertex azure data parallelism strategies latency tuning adaptability automation monitoring rollback kubernetes docker helm orchestration workflow metrics reliability stack aws dynamodb mongodb postgresql devops python sql solver scaling research support recruitment

Work Mode

On-site

Job Type

Full Time

Job Description

This role is for one of our clients

Industry: Human Resources Services
Seniority level: Mid-Senior level

Min Experience: 6 years
Location: Bengaluru, Karnataka, NCR, Delhi
JobType: full-time
We are seeking a highly skilled LLM Systems & MLOps Architect to drive end-to-end architecture, optimization, and deployment of large-scale AI and LLM-based systems. This role is ideal for experts who can blend deep machine learning understanding with strong distributed systems engineering and MLOps capabilities. You will lead the design and implementation of advanced ML production pipelines, optimize GPU-based training and serving infrastructures, and enable efficient delivery of large language model solutions across cloud environments.

What You’ll Own
LLM & ML Pipeline Architecture
Architect and build scalable ML pipelines supporting experiment tracking, model versioning, feature stores, and automated retraining workflows.
Develop high-performance APIs and microservices for real-time model inference and complex multi-model serving environments.
Implement best practices across ML lifecycle management using tools such as MLflow, SageMaker, Vertex AI, and Azure AI.
High-Performance Model Serving & GPU Infrastructure
Design and optimize distributed GPU environments for training and inference of large language models.
Implement model and data parallelism strategies using frameworks such as DeepSpeed, vLLM, and other scalable serving runtimes.
Improve throughput, reduce latency, and optimize resource utilization for large-scale LLM deployments.
Model Fine-Tuning & Performance Optimization
Lead fine-tuning and parameter-efficient training of LLMs and LVMs to enhance accuracy, adaptability, and latency.
Reduce compute cost and training cycle time through advanced optimization methods and architecture improvements.
LLMOps & Production Automation
Implement production-grade automation for training, deployment, monitoring, and rollback using Kubernetes, Docker, Helm, and orchestration systems.
Leverage modern AI workflow frameworks such as Langflow, Flowise, Langgraph, and LangChain for enterprise-scale LLM orchestration.
Build observability, metrics dashboards, and automated reliability systems using Prometheus, Grafana, FluentD, and ELK stack.

Tech Stack & Expertise
LLM Frameworks: Hugging Face transformers, Llama, Mixtral, Claude, GPT, Gemini
LLMOps & Tooling: MLflow, LangChain, LangGraph, LlamaIndex, Flowise, Bedrock, SageMaker, Vertex AI, Azure AI
Cloud: AWS, Azure, GCP
Databases & Warehousing: DynamoDB, Cosmos, MongoDB, RDS, PostgreSQL, Aurora, Spanner, BigQuery
DevOps & Infra: Kubernetes, Docker, Prometheus, Grafana, FluentD, ELK Stack
Languages: Python, SQL, JavaScript
Bonus Certifications: AWS Pro Solutions Architect, AWS ML Specialty, Azure Solutions Architect Expert

Who You Are
Problem-solver passionate about scaling AI systems and pushing boundaries of LLM performance.
Strong communicator who thrives in cross-functional collaboration.
Curious and research-driven, staying current with emerging AI and distributed computing trends.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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