Website:
yallo.co
Job details:
Job Title: Director of AI & Machine Learning
Job Type: Permanent
Location: Bengaluru (India)
Start: ASAP
Job Description:-
Role Summary
Its AI and Machine Learning capability to drive automation, augmentation, and innovation across commercial, digital, operations, and data domains. The Director of AI & Machine Learning is the senior leader accountable for building and running AI/ML function — setting the technical vision, leading the team that designs and ships production-grade AI, and ensuring AI initiatives deliver measurable business outcomes.
This is a leadership role with deep technical credibility. You will lead a team of Enterprise AI Architects, AI Solution Architects, Principal AI Engineers, and AI/ML Developers, and remain close enough to the technology to guide architecture, review designs, and make sound build/buy and model strategy decisions. You will partner with engineering, data, product, and business leaders across the US and the Bengaluru ISSC to operationalize GenAI, agentic AI, and classical ML at scale — grounded in strategic AI framework and an Azure-first platform strategy.
Key Responsibilities
1) AI/ML Strategy & Technical Vision
- Own and evolve AI/ML strategy, roadmap, and reference architecture across GenAI, agentic AI, classical ML, and analytics, aligned to enterprise priorities and an Azure-first platform.
- Set the technical direction for model strategy: model selection and routing, RAG and enterprise search, embeddings and vector strategy, fine-tuning vs. prompting, and agent orchestration patterns.
- Establish AI/ML standards, patterns, and reusable assets (starter kits, SDKs, evaluation harnesses) that accelerate delivery across teams.
- Stay ahead of the GenAI and ML ecosystem and translate emerging techniques into pragmatic, high-value applications.
2) Team Leadership & Capability Building
- Build, lead, mentor, and grow a high-performing AI/ML team across the US and the Bengaluru ISSC, including Enterprise AI Architects, AI Solution Architects, Principal AI Engineers, and AI/ML Developers.
- Own hiring, onboarding, performance management, career development, and succession planning for the AI/ML function.
- Raise AI/ML engineering maturity through design reviews, technical mentorship, and a strong culture of quality, learning, and accountability.
- Define team structure, roles, and ways of working that scale with AI ambitions.
3) Delivery & Execution Ownership
- Be accountable end-to-end for delivery of prioritized AI/ML initiatives — from problem framing and architecture through production deployment, iteration, and measurable impact.
- Own the AI/ML delivery portfolio: intake, prioritization, sequencing, and resourcing against business value.
- Drive delivery of high-value use cases such as AR cash recovery, ISR inquiry automation, procurement and demand planning, pricing intelligence, parts identification, and AI-generated product assets.
- Ensure prototypes consistently mature into secure, reliable, scalable production systems with clear ownership and measurable ROI.
4) Deep AI/ML Technical Depth
- Provide hands-on technical leadership: guide solution designs, review critical architectures and implementations, and resolve the hardest AI/ML problems with the team.
- Maintain working depth across GenAI (RAG, prompt engineering, agents/tool use, evaluation), classical ML (forecasting, classification, optimization), and the supporting data foundations.
- Define and enforce AI evaluation standards — quality, grounding, hallucination detection, drift, and model performance — and the MLOps/LLMOps practices that keep models reliable in production.
- Partner with Data Engineering on AI-ready data foundations: data quality, lineage, metadata, MDM, and domain-aligned data products.
5) Responsible AI, Governance & Risk
- Establish and enforce Responsible AI practices: PII handling and redaction, secure prompt and data policies, audit trails, and human-in-the-loop controls for high-impact actions.
- Define model governance: versioning, evaluation gates, deprecation, and rollback, in partnership with Security and Compliance.
- Own AI risk posture and AI supply-chain awareness, ensuring safe adoption of models, frameworks, and third-party AI services.
6) Executive & Stakeholder Leadership
- Act as a trusted advisor to executives on AI/ML strategy, tradeoffs, build/buy decisions, investment, and delivery sequencing.
- Communicate AI value, risks, and outcomes clearly to both technical and non-technical audiences, including board-level priorities.
- Partner cross-functionally with Digital & eCommerce, Sales (ISR/OSR), Operations, Finance/AR, Procurement, Marketing, and the Data & AI CoE.
- Manage AI/ML budgets and FinOps for AI (cost guardrails, model economics) and oversee relevant vendor and partner relationships.
Essential Duties and Responsibilities
- 15+ years of software engineering / AI / data experience, including 5+ years leading AI/ML or data science teams and delivering AI/ML solutions in production.
- Proven people-leadership track record: building and scaling technical teams, hiring, mentoring, and managing performance — ideally across multiple geographies.
- Deep, demonstrable expertise in AI/ML, including:
- GenAI and LLM systems: RAG, embeddings, prompt engineering, agent/tool use, and evaluation
- Classical ML: forecasting, classification, recommendation, and optimization
- Model lifecycle and MLOps/LLMOps: versioning, evaluation, monitoring, drift, and cost governance
- Strong proficiency with Python (and familiarity with .NET and/or Java ecosystems is a plus).
- Hands-on experience integrating AI/ML into enterprise platforms (CRM, ERP, contact center, eCommerce) using API-first and event-driven patterns.
- Strong command of the Azure AI ecosystem: Azure OpenAI, Azure AI Studio / Azure ML, Databricks / Synapse / Fabric, with sound cloud-native architecture fundamentals.
- Experience with vector databases, enterprise search, and semantic layers / knowledge graphs.
- Experience with agent orchestration frameworks (e.g., LangChain, LangGraph, Semantic Kernel, CrewAI).
- Excellent executive communication, stakeholder management, and governance leadership.
- Builder mindset: creates structure from ambiguity, ties AI/ML work directly to measurable business results, and scales with speed.
Preferred Qualifications
- Experience in distribution / MRO, pricing, supply chain, or customer-experience transformation.
- Experience operating AI/ML systems with direct revenue, margin, or efficiency impact.
- Experience building and operationalizing Data Engineering / AI/ML teams and MLOps practices at scale.
- Familiarity with Epicor P21, NetSuite, Salesforce, or Genesys Cloud.
What Success Looks Like
- A capable, motivated AI/ML team that consistently ships production AI with measurable business impact.
- A clear, adopted AI/ML strategy, reference architecture, and reusable assets that make delivery faster and safer.
- Multiple high-value AI/ML use cases live in production (margin lift, revenue, productivity, CX), with reliable evaluation, monitoring, and cost governance.
- AI/ML is trusted across the business, well governed, and tied directly to measurable outcomes.
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