AI Architect

PeopleStrong; India; On-site; INR 3,000,000 - 7,000,000; Full time

Job summary

PeopleStrong is seeking a seasoned Software Architect with deep expertise in cloud-native GenAI architectures to define, deliver and operate scalable, secure systems at scale. You will lead cross-functional teams to build multi-agent, RAG, LLMOps and AgentOps platforms, ensuring robust integration with enterprise data platforms and secure tool invocations.

You will drive architectural strategy, governance and best practices across the organization, mentoring engineers and shaping the roadmap for

Qualifications

• 10+ years software engineering experience with 3+ years in architecture or senior technical leadership.
• Proven track record designing and delivering cloud-native, production systems at enterprise 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) and Low Level Design Patterns.
• Experience integrating ML/LLM services with enterprise data platforms and APIs while meeting security/compliance requirements.
• Proficiency in at least two languages (Python, TypeScript, Go, Java, C#).

Responsibilities

• Design scalable, cloud-native architectures for GenAI applications (microservices, event-driven, serverless).
• Define data flows, orchestration, and integration patterns for LLMs and tooling.
• Produce architecture artifacts (diagrams, DFDs, API contracts, ADRs).
• Architect and deliver GenAI pipelines including RAG, NL-SQL, and multi-modality.
• Define prompt engineering standards and memory/context models.
• Lead cross-functional teams through architecture reviews and technical decisioning.

Skills

Cloud-native

GenAI/LLM

System design

Multi-agent architectures

Security & compliance

Tools

MLflow

TFX

BentoML

LangChain

OpenAI APIs

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

• 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.
• 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
• 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.
• 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.
• 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.