IN_Manager_Frontier Deployment Consultant (FDC)_GCC_Advisory_Gurgaon

Line of Service
Advisory
Industry/Sector
FS X-Sector
Specialism
Operations
Management Level
Manager
Job Description & Summary
At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth.
Those in intelligent automation at PwC will focus on conducting process mining, designing next generation small- and large-scale automation solutions, and implementing intelligent process automation, robotic process automation and digital workflow solutions to help clients achieve operational efficiencies and reduce costs.
*Why PWC
At PwC, you will be part of a vibrant community of solvers that leads with trust and creates distinctive outcomes for our clients and communities. This purpose-led and values-driven work, powered by technology in an environment that drives innovation, will enable you to make a tangible impact in the real world. We reward your contributions, support your wellbeing, and offer inclusive benefits, flexibility programmes and mentorship that will help you thrive in work and life. Together, we grow, learn, care, collaborate, and create a future of infinite experiences for each other. Learn more
about us
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At PwC, we believe in providing equal employment opportunities, without any discrimination on the grounds of gender, ethnic background, age, disability, marital status, sexual orientation, pregnancy, gender identity or expression, religion or other beliefs, perceived differences and status protected by law. We strive to create an environment where each one of our people can bring their true selves and contribute to their personal growth and the firm’s growth. To enable this, we have zero tolerance for any discrimination and harassment based on the above considerations. "
Role Summary
Drive the design, development, and deployment of enterprise-grade agentic AI solutions built on a strong foundation of Java microservices, cloud-native architecture, and secure, scalable platforms. The role blends Agentic AI engineering with modern backend engineering practices to deliver production-ready, resilient, and governed AI systems, along with the ability to contribute to end-to-end application layers.
Responsibilities
THE MISSION Frontier models and autonomous agent frameworks have moved out of the lab and into the P&L. The binding constraint is no longer model capability — it is deployment. Most enterprise AI programmes stall in the gap between a pilot that impresses a steering committee and a system the business can actually run. The Frontier Deployment Consultant exists to close that gap. You will embed inside client environments — their tenancy, their data, their security review, their release process — and take a workflow from hypothesis to a production agentic system that keeps running after you leave the room. This is a builder-advisor role in the literal sense. You will hold the boardroom with structured business thinking and write the production code that makes the system real. Candidates who have only ever done one of the two will not be a fit.
HOW THE ROLE OPERATES • Embedded, not adjacent. You work inside the client's environment on their infrastructure and release cadence. Expect 50–70% onsite presence, including travel across India, GCC hub cities and client HQ geographies where the engagement requires it. • Pod delivery. FDCs deploy in small pods — typically one Senior FDC plus two to three FDCs and engineers — with access to the practice asset library, PwC's Responsible AI framework and the GCC operating-model IP base. • Outcome-owned. Engagements are measured on time-to-value, reliability, adoption and realised ROI. Delivery milestones are an input, not the score. • Real enterprise conditions. You will work under client security clearance, on client-issued devices where mandated, and inside regulated, data-resident and occasionally air-gapped environments.
WHAT YOU'LL DO 1. Diagnose and shape value Partner with CEOs, COOs, CDOs, CTOs and business unit heads to find the workflows where agentic execution changes unit economics rather than shaving minutes. Size the prize in terms a CFO will act on: cost-to-serve, cycle time, throughput per FTE, revenue leakage, total cost of ownership. 2. Build 0→1 at startup velocity Form the hypothesis, diagnose the workflow with structured consulting toolkits, and put a working, interactive prototype in front of the client in days. The proof is running software, not slideware. 3. Architect production agentic systems Design and implement multi-agent architectures across brownfield estates: planner-executor patterns, agent handoffs, durable state and checkpointing, retries and cancellation, human-in-the-loop escalation. Model Context Protocol (MCP) and agent-to-agent (A2A) integration. Hybrid and graph-based retrieval over knowledge-graph and semantic-layer foundations. 4. Own the evaluation and run layer Build the evaluation harness before the system ships — golden datasets, regression suites, task-completion and toolcall accuracy, groundedness and safety scoring, LLM-as-judge where it is defensible. Instrument tracing end to end. Define and hold service-level objectives for accuracy, latency, cost per task and containment rate. 5. Engineer for the enterprise, not the demo CI/CD for non-deterministic systems, infrastructure as code, containerised deployment into client infrastructure, canary and rollback strategy, runbooks, incident response, and a documented handover that leaves the client's own team selfsufficient. 6. Make it safe and defensible Agent identity and credentialing, least-privilege tool scoping, secrets management, prompt-injection and OWASP LLM Top 10 defences, PII and DLP controls, data residency, full audit trail. Apply PwC's Responsible AI framework and map obligations to the DPDP Act 2023, sectoral regulation (RBI, IRDAI, HIPAA) and EU AI Act risk tiers where in scope. 7. Manage the room Translate non-deterministic behaviour, evaluation results, latency and cost trade-offs and governance constraints into commercially sound executive narratives. Own the working relationship with client CTO, CISO and CFO stakeholders. 8. Operate AI-native Set the benchmark for AI-accelerated delivery on the account — Cursor, Claude Code, GitHub Copilot, agentic test generation, evaluations running in CI — and pull client engineering teams up to it. 9. Codify the IP Abstract deployment patterns into reusable assets: reference architectures, deployment blueprints, evaluation harness templates, governance artefacts, runbooks and role maps that travel across accounts and industries. WHAT YOU WILL BE HELD TO Accountability How it is measured Time-to-value First production workflow live within the mobilisation window agreed at engagement start Reliability Agreed SLOs on accuracy, groundedness, latency and uptime held after go-live, not just at demo Adoption Active usage against the target user base; workflow containment and fall-backto-human rate Unit economics Cost per task inside the agreed envelope; demonstrable TCO improvement against the baseline Self-sufficiency Client team operating and releasing the system without PwC in every change Asset contribution At least one reusable, documented asset contributed to the practice per engagement
WHAT YOU'LL BRING — TECHNICAL Depth is assessed on demonstrated production work. Named tools are illustrative; equivalent experience counts. Core engineering — required • Python to production standard: typing, testing, packaging, profiling. Plus one of TypeScript/Node, Go or Java. • API and service design, microservices, async and event-driven patterns, Docker and Kubernetes. • Working depth in at least one hyperscaler — Azure, AWS or GCP — including networking, identity and cost controls. • Ability to architect hybrid pipelines that route across LLMs, SLMs and traditional ML components based on task fit, cost and reliability requirements Agentic build layer — required • Demonstrated depth in at least two of: LangGraph, Claude Agent SDK, OpenAI Agents SDK, AutoGen, CrewAI, Semantic Kernel, Pydantic AI. • Model Context Protocol (MCP) server and client implementation; agent-to-agent (A2A) interoperability patterns. • Multi-provider abstraction, model routing and fallback (LiteLLM or equivalent). • Long-running and durable execution (Temporal or equivalent), agent memory and state (Mem0, Zep or custom), checkpointing and replay. Retrieval and knowledge — required • Ingestion and chunking pipeline design, embedding lifecycle and re-indexing strategy, reranking, hybrid and graph retrieval, RAG evaluation. • Vector stores: Qdrant, Pinecone, Milvus, pgvector or Azure AI Search. • Knowledge graph and ontology work — Neo4j, GraphRAG, semantic layer design — is a strong differentiator. • Document AI and structured information extraction - extracting structured data from unstructured and semistructured sources (contracts, invoices, forms, scanned records, PDFs) using OCR, layout-aware models and document AI platforms (e.g., Azure Document Intelligence, AWS Textract, Google Document AI) Evaluation, observability and LLMOps — required, and assessed first •