Principal Architect — Cloud, Agentic AI & Data Engineering

Cyient; Hyderabad; Hybrid; INR 4,000,000 - 7,000,000; Full time

Job summary

Cyient is seeking a Principal Architect who can lead end-to-end cloud, data, and agentic AI solutions for strategic engagements, with hands-on delivery and client-facing leadership.

This role owns architecture across Azure/AWS, data platforms, RAG pipelines and LLMOps, shaping proposals, mentoring engineers, and driving reusable accelerators and governance to scale delivery.

Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering or a related discipline.
• 15+ years of IT experience with 5+ years in a formal architect role on large-scale distributed systems.
• Proven delivery of agentic AI or GenAI solutions with architecture ownership.
• Hands-on coding in Python and Java/C#/Scala; able to build a POC when needed.

Responsibilities

• Own end-to-end architecture for large, multi-workstream programs across cloud, data platforms and AI services.
• Design cloud-native solutions on Azure (or AWS) covering compute, networking, identity, observability, cost and security.
• Drive cloud modernisation, landing zones, containerisation and IaC.
• Lead architectural reviews, RFP responses, demos and proofs of concept with the delivery team.
• Mentor architects and engineers; collaborate to create reusable accelerators and standards.

Skills

Azure

AWS / GCP

Python

Java / C# / Scala

Kubernetes

Terraform / IaC

Client-facing

Mentoring

Data engineering

Cost governance

Education

Bachelor's or Master's degree in Computer Science, Engineering or related discipline

Tools

Terraform

Bicep

ARM

Azure Databricks

Microsoft Fabric

Synapse

Snowflake

Job description

Principal Architect — Cloud, Agentic AI & Data Engineering

LOCATION

Hyderabad / Bengaluru / Pune (Hybrid), with travel to client sites as required

EMPLOYMENT TYPE

Full-time, Permanent

ROLE SUMMARY

Cyient is looking for a Principal Architect who can operate at the intersection of cloud platform engineering, agentic AI and modern data engineering — and who is equally comfortable in front of a customer as in a design review.

This is a hands‑on, customer‑facing architecture role. You will own end‑to‑end solution architecture for strategic engagements, shape winning proposals and RFP responses, build demos and proofs of concept alongside the delivery team, and set the technical direction that engineering teams execute against. You will also mentor architects and senior engineers, and partner with fellow architects across practices to drive reusable assets, accelerators and an active innovation agenda.

Success in this role looks like architecture decisions that survive production, customers who ask for you by name, proposals that convert, and a bench of engineers who are measurably stronger because you invested in them.

KEY RESPONSIBILITIES

• Own end-to-end architecture for large, multi-workstream programs spanning cloud infrastructure, data platforms and AI services.
• Design cloud-native solutions on Microsoft Azure (or AWS ) covering compute, networking, identity, integration, observability, cost management and security.
• Define the non-functional architecture — scalability, resilience, latency, availability, disaster recovery and multi-tenancy — and hold designs accountable to it.
• Establish architecture governance: reference architectures, design standards, review checkpoints, a technology radar and architecture decision records (ADRs).
• Drive cloud modernisation and migration strategy — landing zones, containerisation, microservices, event-driven patterns and infrastructure-as-code (Terraform / Bicep / ARM).

Agentic AI & Applied GenAI

• Architect agentic AI systems — multi-agent orchestration, tool and function calling, planning and reflection loops, memory, and human-in-the-loop control points.
• Select and integrate the AI stack: LLM providers (Azure OpenAI, Anthropic Claude, open-weight models), orchestration frameworks (LangGraph, Semantic Kernel, AutoGen, MCP-based tooling) and vector / retrieval layers.
• Design production-grade RAG and knowledge-grounding pipelines — chunking strategy, embeddings, hybrid search, re-ranking, grounding and citation.
• Define AI evaluation and guardrails: accuracy and hallucination benchmarks, red-teaming, prompt-injection defence, PII handling, cost and token governance, and model observability.
• Operationalise LLMOps — prompt versioning, CI/CD for AI assets, regression evaluations, drift monitoring and safe rollout patterns.
• Champion Responsible AI principles — transparency, traceability, bias assessment and auditability — aligned to client and regulatory expectations.
• Architect modern data platforms — lakehouse and medallion architectures on Azure Databricks, Microsoft Fabric, Synapse, Snowflake or equivalent.
• Design batch and streaming pipelines using Spark, Kafka / Event Hubs, Azure Data Factory and dbt, with clear SLAs on freshness and data quality.
• Establish data governance: cataloguing, lineage, master data management, quality frameworks and access control (Unity Catalog, Microsoft Purview).
• Model data for both analytics and AI consumption, ensuring the same governed assets serve BI, ML and agentic workloads.
• Optimise for cost and performance — partitioning, storage tiering, compute right-sizing and workload isolation.
• Act as the senior technical face to the customer — run discovery workshops, architecture reviews and executive briefings for both engineering and CxO audiences.
• Translate business problems into solution options, with honest trade-offs on cost, risk, time-to-value and long-term maintainability.
• Lead technical responses to RFPs, RFIs and proactive proposals — solution narrative, architecture diagrams, delivery approach, effort estimation, staffing model and risk register.
• Build compelling demos and proofs of concept with the team — tightly scoped, built fast, and mapped to a measurable customer outcome.
• Support deal shaping alongside sales and account teams: value articulation, differentiation, competitive positioning and defence of the proposed architecture.
• Own the technical narrative post-win, ensuring that what was sold is what gets designed and delivered.
• Guide and mentor architects, tech leads and senior engineers through design reviews, pairing, structured feedback and career conversations.
• Collaborate with peer architects across practices and geographies to align on standards, resolve cross-domain design conflicts and avoid duplicated effort.
• Drive the innovation agenda: identify emerging technologies, run experiments and spikes, and convert successful ones into reusable accelerators, frameworks and IP.
• Build the capability bench — define skill paths, run internal enablement sessions and raise the architectural maturity of the practice.
• Represent Cyient externally through whitepapers, conference talks, partner forums and customer advisory sessions.

REQUIRED QUALIFICATIONS & EXPERIENCE

• Bachelor's or Master's degree in Computer Science, Engineering or a related discipline.
• 15+ years of overall IT experience, including 5+ years in a formal architect role on large-scale distributed systems.
• Deep, hands‑on expertise with Azure (preferred) or equivalent depth in AWS or GCP — demonstrable production ownership, not design-deck experience alone.
• Proven delivery of at least one production agentic AI or applied GenAI solution, from architecture through evaluation, guardrails and go‑live.
• Strong data engineering background: lakehouse architectures, distributed processing (Spark), streaming and enterprise-scale data governance.
• Demonstrated presales and customer-facing track record — owned technical solutioning on proposals and RFPs that converted.
• Hands‑on coding credibility in Python and one of Java / C# / Scala; able to build a POC personally when it matters.
• Experience with DevOps and platform engineering practices: CI/CD, IaC, containerisation, Kubernetes and observability tooling.
• Track record of mentoring senior technical talent and leading architecture communities of practice.
• Excellent written and verbal communication — able to hold a room of engineers and a room of executives with equal effect.

PREFERRED QUALIFICATIONS

• Azure certifications: Solutions Architect Expert (AZ-305), Data Engineer Associate (DP-203) or AI Engineer Associate (AI-103); equivalent AWS certifications equally valued.
• Databricks, Snowflake or Microsoft Fabric certification.
• Experience with the Model Context Protocol (MCP), agent interoperability standards and enterprise agent platforms.
• Domain exposure in one or more Cyient focus verticals: Aerospace & Defence, Rail & Transportation, Energy & Utilities, MedTech / Healthcare, Semiconductor or Communications.
• Experience building reusable accelerators, platforms or IP adopted across multiple accounts.
• Prior work in a services, consulting or systems-integrator environment with multi-client, multi-geography delivery.
• Published thought leadership, patents or active open-source contribution.

TECHNICAL SKILLS SNAPSHOT

Area

Azure (AKS, Functions, App Service, API Management, Event Hubs, Key Vault, Entra ID, Monitor); AWS or GCP equivalents

Technologies & Practices

Azure (AKS, Functions, App Service, API Management, Event Hubs, Key Vault, Entra ID, Monitor); AWS or GCP equivalents

Agentic AI / GenAI

Azure OpenAI, Anthropic Claude, open-weight LLMs; LangGraph, Semantic Kernel, AutoGen, MCP; RAG, vector stores, evaluations, guardrails, LLMOps

Consulting Skills

Discovery workshops, solution storytelling, estimation, RFP response, POC and demo build, executive communication