Lead Architect

1 month ago

Pune, India +2 moreStaff+

Responsibilities

Architect and build scalable LLMOps platforms for enterprise-grade GenAI systems.
Design and manage end-to-end LLM pipelines covering data ingestion, embeddings, evaluation, fine-tuning, and inference.
Develop, deploy, monitor, and optimize ML and LLM systems for production at scale.
Build agentic AI workflows and operational capabilities including orchestration, evaluation, observability, and reflection loops.
Implement guardrails, output filtering, context-aware routing, evaluation harnesses, metrics logging, and incident response.
Create scalable Kubernetes- and GPU-aware deployment frameworks for LLM inference across cloud and hybrid environments.
Drive platform automation using Docker, Kubernetes, GitOps, Terraform, and DevOps practices.
Design systems for prompt and model versioning, AI governance, automated testing, and prompt quality scoring.
Lead ideation, prototyping, and scaling of reusable internal LLMOps accelerators.
Provide technical leadership and lead development teams.

10–14 years of experience working on ML projects, including product building, hands-on engineering, technical leadership, and leading development teams.
Experience with model development, training, deployment at scale, and production performance monitoring.
Strong Python, data engineering, FastAPI, and NLP skills.
Knowledge of LangChain, LlamaIndex, Langtrace, Langfuse, LLM evaluation, MLflow, and BentoML.
Experience with proprietary and open-source LLMs and LLM fine-tuning, including PEFT and CPT.
Experience creating agentic AI workflows using CrewAI, LangGraph, AutoGen, and Semantic Kernel.
Experience with performance optimization, RAG, guardrails, AI governance, prompt engineering, evaluation, and observability.
Experience deploying GenAI applications at scale in cloud and on-premises environments using DevOps and MLOps practices.
Working knowledge of Kubernetes and Terraform and experience with at least one of AWS, GCP, or Azure.
Experience with Ray, Truss, OpenAI Evals, Ragas, Rebuff, Outlines, Helm, Docker, Airflow, Prefect, Feast, Spark, Flink, Parquet, Delta, Azure ML, Vertex AI, AWS Bedrock, or SageMaker.
Experience with LLMOps, prompt evaluation, cloud-native deployment, ML pipelines, data platforms, and scalable inference architectures.
Python is required; Bash, YAML, and Terraform HCL are preferred.
Strong communication, presentation, collaboration, and product-thinking skills.

5,001-10,000 employees