Ai llmops architect

Company Description Collabrah Tech Solutions is an innovative IT company specializing in IT consulting, staffing, and recruiting. Our expertise encompasses a wide range of services including analytics, data science, data engineering, machine learning, artificial intelligence, cloud migration, managed services, and more. We serve clients across various industries such as banking & financial services, e Commerce, retail, CPG, engineering, healthcare, and pharma. At Collabrah Tech Solutions, we are dedicated to delivering high-quality solutions and services to meet the diverse needs of our clients. Role Description This is a full-time on-site role for an AI LLMOps Architect based in Bengaluru / Chennai / Gurgaon / Mumbai / Pune. Role overview: We’re building a next-gen LLMOps team to industrialize Gen AI implementation and shape the future of Gen AI engineering. This is a hands-on technical leadership role for AI engineers with strong ML and Dev Ops skills — ideal for those who love building scalable systems from the ground up. You will be designing, deploying, and scaling Gen AI and Agentic AI applications with robust lifecycle automation and observability.   Required Qualifications: 11-16 years of experience in working on ML projects that includes product building mindset, strong hands on skills, technical leadership, leading development teams Model development, training, deployment at scale, monitoring performance for production use cases Strong knowledge on Python, Data Engineering, Fast API, NLP Knowledge on Langchain, Llamaindex, Langtrace, Langfuse, LLM evaluation, MLFlow, Bento ML Should have worked on proprietary and open-source LLMs Experience on LLM fine tuning including PEFT/CPT Experience in creating Agentic AI workflows using frameworks like Crew AI, Langraph, Auto Gen, Symantec Kernel Experience in performance optimization, RAG, guardrails, AI governance, prompt engineering, evaluation, and observability Experience in Gen AI application deployment on cloud and on-premises at scale for production using Dev Ops practices Experience in Dev Ops and MLOps Good working knowledge on Kubernetes and Terraform Experience in minimum one cloud: AWS / GCP / Azure to deploy AI services Team player with excellent communication and presentation skills Must have skills: Product thinking that includes ideation, prototyping, and scale internal accelerators for LLMOps Architect and build scalable LLMOps platforms for enterprise-grade Gen AI systems Design and manage end-to-end LLM pipelines   from data ingestion and embedding to evaluation and inference Drive LLM-specific infrastructure :  memory management, token control, prompt chaining, and context optimization Lead scalable deployment frameworks for LLMs using Kubernetes and GPU-aware scaling Build agentic AI operations capabilities including agent evaluation, observability, orchestration and reflection loops Guardrails & Observability: Implement output filtering, context-aware routing, evaluation harnesses, metrics logging, and incident response Platform Automation for LLMOps: Drive end-to-end automation with Docker, Kubernetes, Git Ops, Dev Ops, Terraform, etc. Product Thinking : Ideate, prototype, and scale internal accelerators and reusable components for LLMOps Gen AI Engineering : Productionize LLM-powered applications with modular, reusable, and secure patterns Pipeline Architecture : Create evaluation pipelines — including prompt orchestration, feedback loops, and fine-tuning workflows Prompt & Model Management : Design systems for versioning, AI governance, automated testing, and prompt quality scoring Scalable Deployment : Architect cloud-native and hybrid deployment strategies for large-scale inference Guardrails & Observability : Implement output filtering, context-aware routing, evaluation harnesses, metrics logging, and incident response Dev Ops & Platform Automation : Drive end-to-end automation with Docker, Kubernetes, Git Ops, Terraform, etc. Must-Have Technical Skills LLMOps frameworks : Lang Chain, MLflow, Bento ML, Ray, Truss, Fast API Prompt evaluation and scoring systems : Open AI evals, Ragas, Rebuff, Outlines Cloud-native deployment : Kubernetes, Helm, Terraform, Docker, Git Ops ML pipeline : Airflow, Prefect, Feast, Feature Store Data stack : Spark/Flink, Parquet/Delta, Lakehouse patterns Cloud : Azure ML, GCP Vertex AI, AWS Bedrock/Sage Maker Languages : Python (must), Bash, YAML, Terraform HCL (preferred)