Solutions Architect - Generative AI

Tata Communications Limited; Bengaluru; Presencial; INR 4.000.000 - 7.000.000; Jornada completa

Descripción de la vacante

Tata Communications Limited is seeking an AI OS architect to turn complex enterprise data into a unified ontology. You will wire data and systems, orchestrate LLM-driven agents, and ensure governance, security, and cost discipline on customer deployments.

Role requires hands-on experience with enterprise AI platforms and a proven ability to design forward-deployed, scalable AI solutions in large customer environments. Travel to client sites will be necessary.

Formación

• Must have architected and shipped an enterprise AI platform deployment in a forward-deployed or embedded setting.
• Ontology-first thinking with reusable semantic layer, metadata, permissions and scalable data structures.
• Deep data engineering: pipelines, lakehouse, lineage, data quality, access controls.
• LLM/agent orchestration, context engineering, tool use, evals, guardrails.
• Enterprise governance: IAM, permissions, model governance, SOC2/ISO27001/GDPR awareness.
• Forward-deployed temperament with customer outcomes, travel, ambiguity tolerance and strong docs.

Responsabilidades

• Lead discovery at customer sites: map objects, relationships, actions, and processes into a shared ontology.
• Architect data foundation: pipelines from systems into governed data layer with lineage and controls.
• Design for reuse: model objects/actions once, versioned and composable across AI workflows.
• Design the AI layer: RAG, context engineering, agent orchestration, tool-calling, evals.
• Architect agentic workflows: autonomous, supervised, and human-in-the-loop.
• Own model governance and cost per action, prompt/version control, guardrails, drift monitoring.

Conocimientos

AI OS architecture

Ontology-first

Data engineering

LLM orchestration

Governance & security

Forward deployment

Descripción del empleo

Locations: Bangalore, Mumbai, Pune and Gurgaon

About the Role

Enterprises no longer buy AI features — they buy an AI operating system: a layer that unifies their data, their decisions, and their AI agents into one governed, auditable machine. Tata Comm AI OS proved the model with Context Graph and AIP — an ontology-first architecture where every data asset, action, and process is modelled once and reused everywhere.

You are not an internal software architect. You are the AI OS architect on the customer’s floor — who turns their messiest operational reality into a clean ontology, wires their data and systems into it, and orchestrates LLM-driven agents and workflows on top, all within governance, security, and cost discipline. You should have lived this, or built the same discipline with other enterprise AI platforms (Palantir, Databricks, Snowflake, AWS/Azure/GCP AI stacks, or an in-house equivalent).

Key Responsibilities

• Enterprise Ontology & Data Architecture (the core of the role)
• Lead discovery at customer sites: map the enterprise’s objects (customers, orders, assets, claims, equipment), their relationships, actions, and processes — then model them into a shared, reusable ontology, not a pile of point solutions.
• Architect the data foundation: pipelines from operational systems, ERP/CRM, telemetry, documents and knowledge bases into a governed data layer (zero-copy / lakehouse patterns), with lineage, quality rules, and access controls that hold up to audit.
• Design for reuse: every object, action, and process modelled once — permissioned, versioned, and composable across all downstream AI workflows.
• Design the AI layer on the enterprise OS: RAG over enterprise knowledge, context engineering, LLM/agent orchestration, tool-calling, human-in-the-loop fallbacks, and evaluation loops — using no-code, low-code and pro-code paths as the situation demands.
• Architect agentic workflows (autonomous, supervised, and human-in-the-loop)
• Own model governance: model selection and cost per action, prompt/version control, guardrails, safety and bias checks, drift monitoring, and audit trails.

Forward-Deployed Delivery

• Be the technical lead embedded in 1–2 major customer engagements at a time: run joint design workshops, build the reference architecture, and stay on-site through launch — wrangling real, messy, production data alongside customer teams.
• Build the thin slice that proves the model early (one decision, one agent, one happy path), then expand — demonstrating value in weeks, not quarters.
• Feed field learnings back: what patterns win in the market becomes input to our product and portfolio teams.

Must-Have (Non-Negotiable)

1. Relevant AI OS architecture experience — mandatory. You must have architected and shipped an enterprise AI platform deployment in a forward-deployed or equivalent embedded setting: at Palantir, Databricks, Snowflake, AWS/Azure/GCP AI platforms, or a comparable in-house enterprise AI platform. Generic ML/DS experience without enterprise-deployment depth will not clear the bar.

2. Ontology-first thinking — demonstrated ability to model an enterprise’s objects, actions and processes as a reusable semantic layer, with metadata, permissions and data structures kept consistent, secure, and scalable Ontology.

3. Deep data engineering — pipelines, lakehouse/data-platform architecture, streaming and batch, data quality and lineage, zero-copy patterns; SQL, PySpark or equivalent fluency.

4. LLM/agentic AI depth — RAG architectures, context engineering, prompt and agent orchestration, tool use, evals, guardrails, and cost/latency tuning; hands‑on with at least one major LLM stack.

5. Enterprise governance & security — IAM, row/object‑level permissions, model governance, compliance (SOC2/ISO27001/GDPR and India DPDP Act awareness), and speaking that language to CTOs and CISOs.

6. Forward‑deployed temperament — you are measured by customer outcomes, you travel, you tolerate ambiguity, and you can explain architecture to a COO and run a terminal in the same afternoon. Excellent written documentation and client‑facing communication.