We are looking for a seasoned MLOps Engineer with strong technology and consulting experience to join the AI/ML Practice team at Intellias. Join us to blueprint GenAI platforms and establish LLMOps practices for client teams across industries, partnering from presales and discovery through PoC to production.Requirements 7+ years in MLOps/platform architecture or adjacent roles, with shipped AI systems Proficient Python and strong software engineering principles Deep experience with at least one major cloud (AWS/Azure/GCP) and platform engineering (containers, Kubernetes, IaC such as Terraform) Experience in designing and guiding scalable machine learning pipelines for model training, validation, and deployment Proven CI/CD design for GenAI/ML (evaluation gates, versioning, canary, rollback) and collaboration with security/governance stakeholders Sound judgement selecting RAG/vector and provider stacks based on performance, cost, compliance, and portability Agent orchestration frameworks (e.g., LangGraph/Semantic Kernel) and tooling protocols (e.g., MCP) Experience operationalizing multi-agent systems (tools/routing/memory/guardrails, human-in-the-loop) Process automation and enterprise integrations Excellent communication and interpersonal skills to collaborate effectively with cross-functional teams, stakeholders' leadership Upper-intermediate level of EnglishNice to have: Master or higher degree in Computer Science, Engineering, or related field On-prem LLM deployments; performance and cost tuning with caching and model routing AI safety, policy, and compliance experience in sensitive environments Public speaking and enablement and building reusable accelerators Domain exposure in automotive, retail, manufacturing, healthcare, energy, finance, or telecomResponsibilities Lead discovery with stakeholders and define adoption roadmaps and reference architectures Set lifecycle practices for GenAI (LLMOps) Architect retrieval and provider layers (RAG, vector stores, model gateways) with portability, cost, and compliance in mind Implement RAG/agent workflows that orchestrate tool-calling, retrieval, and grounded answering Enable agentic applications at platform level and define solution patterns and evaluation gates (standardized tools, routing, shared memory, HIL, safe fallbacks) aligned with enterprise integration, security, and cost Set standards for ingestion, chunking, embedding, and indexing pipelines; select and tune vector databases for retrieval Establish CI/CD, Infrastructure-as-Code, observability, and automated testing Define governance and safety guardrails Establish environment strategy and promotion paths, and a clear handover plan to client teams Package reusable patterns/accelerators, mentor engineers, and support presales and proposals