Manager - Customer Interaction Suite

About The Company
Tata Communications Redefines Connectivity with Innovation and IntelligenceDriving the next level of intelligence powered by Cloud, Mobility, Internet of Things, Collaboration, Security, Media services and Network services, we at Tata Communications are envisaging a New World of Communications
Job Description — AI OS Architect, Individual Contributor
Company: Tata Communications
Function: Solution Engineering
Role Type: Individual Contributor (IC) — senior, no people management. An architect embedded with enterprise customers to design and deploy their AI operating system.
Locations: India (Mumbai, Pune, Bengaluru, Gurugram)
UK (London)
USA (New York / New Jersey, Dallas, or remote within the US) — one global requisition
Experience: 8+ years, with a minimum of relevant AI platform architecture experience (Palantir Foundry/AIP or equivalent) — relevant experience is mandatory
Travel: Up to 40–50% — this is a forward-deployed role; you will live at customer sites during major deployments, both domestic and international
About The Role (a Note From The Hiring Head)
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. If you 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), I want to talk.
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.
AI Platform & Agent Architecture
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)
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.
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.
Deep data engineering — pipelines, lakehouse/data-platform architecture, streaming and batch, data quality and lineage, zero-copy patterns; SQL, PySpark or equivalent fluency.
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.
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.
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.
What Success Looks Like (KPIs)
Customer engagements that reach production: number of deployments delivered from architecture to go-live at the customer site.
Time-to-first-value: weeks from kickoff to a production thin slice per engagement.
Ontology/platform reuse: patterns and assets built once, reused across multiple customers.
Deal technical win rate and accuracy of BoM/effort estimates versus contracted scope.
Field feedback that demonstrably shaped product roadmap.

Work arrangement
No