Head - AI Practice

Role Summary :

Lead the end-to-end AI Practice and convert the division into an AI-native organization across GTM, delivery, platformization, governance, and talent. This is a builder/operator role responsible for creating market-ready AI offerings and solutions, establishing an internal AI platform and accelerators to industrialize delivery, and embedding responsible AI and risk controls so we can scale adoption across regulated and non-regulated clients. You will own measurable outcomes : AI-led bookings, pipeline conversion, margin, reuse of accelerators, and delivery reliability.

Key Responsibilities :

1. Build the AI-Native Division (Operating Model Transformation) :

- Define and roll out an AI-native operating model across presales, delivery, engineering, and support (AI-first SDLC patterns, prompt/agent engineering standards, reusable component libraries).

- Drive internal adoption playbooks for Sales, Presales, Delivery, PMO, ITO, and QE to accelerate enterprise-wide AI usage.

- Stand up an AI Governance & Enablement Council to enforce standards, safety, and repeatability (avoid tool sprawl).

2. Create & Take AI Offers to Market (Signature Offers + Vertical Plays) :

- Build a portfolio of signature AI offers with clear scope boundaries, outcomes, pricing guardrails, and delivery playbooks (e.g., Enterprise GenAI Enablement/RAG, Knowledge Assist, Agentic Automation, Document Intelligence/IDP, AI-led Modernization, Responsible AI readiness).

- Launch verticalized plays (BFSI/HCLS/Consumer/Hi-Tech) with domain narratives, compliance framing, and proof packs.

- Own assets that win : reference architectures, one-pagers, pitch narratives, proposal modules, ROI logic, and demo/POV kits.

3. Platform + Accelerators (Industrialize AI Delivery) :

- Build an internal AI platform/toolkit to accelerate delivery : RAG reference architectures, retrieval patterns, evaluation harnesses, guardrails, telemetry/observability, and standardized deployment patterns.

- Drive reuse metrics : percentage of proposals and deliveries using standard patterns/accelerators; reduced cycle time and improved predictability.

4. Pursuit Leadership (Solutions + Estimation + POV-to-Program Conversion) :

- Lead AI solutioning for pursuits : discovery, use-case qualification, architecture, estimation, staffing pyramid, risks/assumptions, and pricing/GM guardrails.

- Run workshops/POVs as a conversion product with clear success criteria and a scalable program plan.

5. Delivery Excellence & Profitability (AI Programs at Scale) :

- Own delivery health across AI programs : quality, predictability, and GM realization.

- Establish LLMOps/MLOps discipline : testing, evaluation, monitoring, incident handling, change control, and release governance.

6. Responsible AI, Risk, and Compliance-by-Design :

- Operationalize practical AI governance : data governance, privacy/security, model evaluation (hallucination/bias/toxicity risk), human oversight, logging, and audit-ready evidence.

- Implement controls suited to regulated clients while maintaining speed-to-value.

7. Partnerships & Ecosystem (Revenue Through Alliances) :

- Drive partner-led GTM with hyperscalers (Microsoft/Azure, AWS, Google Cloud) including co-sell plays, enablement, and specialization roadmaps.

- Curate and manage AI/data/automation ecosystem partners to support offerings (LLM providers, data platforms, workflow/RPA, observability and security).

- Run a partner scorecard : sourced pipeline, influenced bookings, attach rate, and joint POV conversions.

8. Talent, CoE Build-out, and Enablement :

- Build the AI practice org : solution architects, data/ML engineers, prompt/agent engineers, platform engineers, AI QA/eval specialists.

- Create certification pathways, internal guilds, hiring rubrics, and a strong bench-to-billable conversion engine.

Areas of Expertise / Experience :

- 15-18+ years in IT Consulting/ engineering/data

- 4-5 Years in AI Practice building. Experience in Enterprise AI enablement.

- 5+ years leading practices or large programs in IT services/product engineering

- Hands-on depth in GenAI + applied AI delivery (RAG, agentic patterns, evaluation/guardrails, production deployment)

- Proven experience building market offers, winning deals, and running delivery with margin discipline

- Comfortable in regulated environments (BFSI/HCLS) with governance and audit readiness.

Tool Expertise & Platforms (expected / preferred) :

- Azure AI / AWS / Google Cloud (architecture + deployment)

- LLMOps / MLOps

- CI/CD for AI apps, evaluation pipelines, monitoring/telemetry, incident handling, change control

RAG Stack :

- Vector databases/retrieval patterns, embedding strategy, caching, data connectors

Agent Orchestration :

- Agent frameworks/orchestrators; workflow and tool integrations

Observability :

- Logging/telemetry for prompts, retrieval, tool calls, model responses; dashboards and alerting

Security :

- IAM patterns, secrets management, secure SDLC, SAST/DAST, threat modeling for AI applications

Data Platforms :

- Lakehouse/warehouse patterns (e.g., Databricks/Snowflake) and governance integration

Success Metrics :

- GTM : AI bookings and qualified pipeline; win-rate on AI pursuits; POV-program conversion

- Adoption : number of accounts adopting AI offers; cross-sell penetration into existing accounts

- Platformization : accelerator reuse percentage; cycle-time reduction; proposal module reuse

- Delivery : GM realization; estimation variance control; delivery CSAT; production stability

- Governance : evaluation coverage, logging/evidence readiness, incident handling maturity for regulated clients

- Talent : certifications, utilization/pyramid health, retention, and leadership bench depth

(ref:hirist.tech)