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.