AI Architect

Job Description We’re looking for a seasoned Software Architect with deep expertise in cloud-native enterprise systems and Generative AI. You will define and deliver scalable, secure, and production-grade GenAI architectures — including multi-agent, RAG, LLMOps and AgentOps systems — and lead cross-functional teams to build and operate them. This role combines hands-on technical leadership, systems thinking, and strong stakeholder management. Key responsibilities Architecture & System Design - Design scalable, modular, and cloud-native architectures for GenAI applications(microservices, event-driven, serverless). - Define system boundaries, data flows, orchestration, and integration patterns forLLMs, vector DBs, embedding services, and tool integrations. - Produce architecture artifacts ( Layered Architecture Diagrams, C4 Models, DFDs,Class, Sequence, ER & Use Case diagrams, different types of blueprints, APIcontracts, design and trade-off decisions). - GenAI & Agentic Systems - Architect and deliver Retrieval-Augmented Generation (RAG) pipelines, NaturalLanguage to SQL Flows, fine-tuning strategies, multi-modal capabilities, and tool-augmented agents. - Design agent orchestration and multi-agent frameworks enabling planning,reasoning, and secure tool invocations, implement and design Agent prototypes andCommunication Protocols. - Define prompt engineering standards, memory models(episodic/semantic/procedural), and context management. LLMOps & AgentOps - Define and implement model lifecycle pipelines: training, fine-tuning, validation,deployment, rollback, and monitoring. - Build AgentOps processes for agent lifecycle, behavior tracking, governance andperformance optimization. - Automate CI/CD for models, agents and services (MLflow, TFX, BentoML, custompipelines). Integration, Security & Compliance - Integrate GenAI services with enterprise systems (ERP, CRM, data lakes, APIs) usingsecure, scalable interfaces. - Ensure secure access controls, data privacy, encryption, and compliance (GDPR,HIPAA, SOC2). - Define responsible AI practices: bias mitigation, explainability, audit trails, andoutput governance. - GenAI security — classify, encrypt & sign data/models; enforce least-privilege withshort-lived creds and CI/CD security gates; telemetry, drift/hallucination alerts, kill-switch & runbooks. - Agentic AI security — provable agent identity/attestation, tool allowlist + human gatefor high-risk actions; ephemeral scoped tokens, sandboxed execution,and replayable audit traces. Observability, Ops & Cost Optimization - Define telemetry, tracing, and logging for models and agents; monitor performance,drift, hallucination rates and user feedback loops. - Build dashboards, alerts and runbook guidance for operational health. - Design systems for cost efficiency (autoscaling, spot instances, serverlesschoices) and support FinOps practices. Leadership, Collaboration & Documentation - Lead cross-functional teams (product, data science, AI engineers, platform)through architecture reviews, workshops, and technical decisioning. - Maintain architectural standards, documentation, playbooks, and patternlibraries for GenAI systems. - Mentor engineers and evangelize best practices across the organization. Required qualifications & experience - 10+ years software engineering experience with 3+ years in architecture or seniortechnical leadership roles (or equivalent). - Proven track record designing and delivering cloud-native, production systems atenterprise scale. - Hands-on experience with GenAI/LLM systems, RAG, NL-SQL,agentic frameworks or similar productionized AI applications. - Strong knowledge of system design patterns (microservices, event-driven,CQRS, hexagonal architecture), and Low Level Design Patterns. - Experience integrating ML/LLM services with enterprise data platforms and APIswhile meeting security/compliance requirements. - Solid engineering background in at least two languages (Python, TypeScript, Go,Java, C#) and familiarity with modern frameworks. Technical skills & technologies (comprehensive) - Cloud & Infra: AWS / Azure / GCP; Kubernetes, Docker, serverless (Lambda, Functions, Cloud Run), GPU instances - GenAI & ML: Hugging Face Transformers, OpenAI APIs, ,LangChain, LlamaIndex, Semantic Kernel, Haystack - Vector Stores: FAISS, Pinecone, Weaviate, Chroma, Postgres+pgVector, and other cloud vector stores - LLMOps / MLOps: Custom Development of Ops Pipelines, MLflow, TFX, BentoML, Kubeflow - Data & Integration: Kafka, Spark, Airflow, Flink, ETL/ELT concepts, data lakes, API gateways (Apigee etc) - DevOps & IaC: Terraform, Pulumi, CloudFormation, GitHub Actions, Jenkins - Observability & Security: Prometheus, Grafana stack, OpenTelemetry, Jaeger, ELK, Datadog; Vault, - IAM, LDAP/OAuth2/OIDC/SAML Connect, Snyk, SonarQube, SAST/SCA in pipelines, OWASPs, CWEs, CVEs. - Databases & Storage: Relational (RDS/Cloud SQL), NoSQL (Mongo, DynamoDB, Cosmos DB), Redis, S3/Blob/GCS, ORM/ODM frameworks. - Agent frameworks / tools: Understanding of Basics of Agents required, Langgraph, Autogen, AutoGPT, AgentVerse, MetaGPT, CrewAI etc. - Performance & scalability: SSR/ISR, caching strategies (CDN, edge), lazy loading, bundle optimization, performance budgets. - Realtime & asyncRealtime & async: WebSockets, SSE, message brokers (Kafka, RabbitMQ), background workers. Frontend frameworks: React (Next.js), Angular, Vue; component libraries and state (Redux/RTK, Context, Pinia, Zustand) - Styling & UI tooling: Component Libraries, Accessibility best practices, Responsive UI - Frontend build & tooling: Vite, Webpack, Storybook, UI Frameworks. - Backend frameworks: Node.js/Express, FastAPI, serverless functions (AWS Lambda, Cloud Functions) - API design & integration: REST, gRPC, OpenAPI/Swagger, API versioning and contract testing, GraphQL(Optional) - UX & product mindset: Design-system familiarity, usability, accessibility, and working with designers Behavioral & leadership skills - Strategic thinking with the ability to align architecture to product and businessgoals. - Excellent communicator: simplify complex technical concepts for technical andnon-technical stakeholders. - Strong mentorship skills — able to raise team capability in GenAI architectureand engineering. - Pragmatic decision-maker with a bias for measurable outcomes and trade-offanalysis. - High attention to detail, ownership, and accountability for reliability, security,and cost. Nice-to-have - Experience operating LLMs in regulated industries (pharma). - Familiarity with prompt auditing, hallucination detection, and automated qualitychecks. - Background in knowledge engineering, semantic search, or knowledge graphs. - Academic background in CS, ML, or equivalent applied experience. - Mobile & cross-platform (optional): React Native, Flutter basics for mobileintegration - Deliverables & success metrics (examples) - Production-ready GenAI architecture and deployment runbook. - Deployed RAG/agent pipeline with observable SLOs and monitoring dashboards. - Reduced model hallucination/incidents and measurable improvement inretrieval quality. - Architecture decision records (ADRs), standards library, and cross-teamonboarding materials. - Cost targets achieved through optimized infra and autoscaling policies.