Required Qualifications
- 10 - 14 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, FastAPI, NLP
- Knowledge on Langchain, Llamaindex, Langtrace, Langfuse, LLM evaluation, MLFlow, BentoML
- 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 CrewAI, Langraph, AutoGen, Symantec Kernel
- Experience in performance optimization, RAG, guardrails, AI governance, prompt engineering, evaluation, and observability
- Experience in GenAI application deployment on cloud and on-premises at scale for production using DevOps practices
- Experience in DevOps 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 GenAI 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, GitOps, DevOps, Terraform, etc.
Product Thinking: Ideate, prototype, and scale internal accelerators and reusable components for LLMOps
GenAI 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
DevOps & Platform Automation: Drive end-to-end automation with Docker, Kubernetes, GitOps, Terraform, etc.
Must-Have Technical Skills
- LLMOps frameworks: LangChain, MLflow, BentoML, Ray, Truss, FastAPI
- Prompt evaluation and scoring systems: OpenAI evals, Ragas, Rebuff, Outlines
- Cloud-native deployment: Kubernetes, Helm, Terraform, Docker, GitOps
- ML pipeline: Airflow, Prefect, Feast, Feature Store
- Data stack: Spark/Flink, Parquet/Delta, Lakehouse patterns
- Cloud: Azure ML, GCP Vertex AI, AWS Bedrock/SageMaker
- Languages: Python (must), Bash, YAML, Terraform HCL (preferred)
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!