">AI ML Practice Leader

15-25 Years Noida

- AI ML
- Business Development

- AI/ML Practice Head

Location: Noida

Experience: 15+ Years

Employment Type: Full-Time

Work Mode: Work from Office / Hybrid as per business requirements

About the Role

We are looking for an experienced AI/ML Practice Head to build, lead, and scale our Artificial Intelligence and Machine Learning practice.

This is a business and technology leadership role with end-to-end responsibility for practice strategy, revenue growth, profitability, client engagement, solutioning, delivery excellence, capability development, and team building .

The ideal candidate should combine strong AI/ML and Generative AI expertise with commercial acumen and proven experience in building enterprise AI solutions, developing market-facing offerings, growing strategic accounts, and scaling high-performing AI/ML teams.

The candidate will work closely with Sales, Delivery, Technology, Talent Acquisition, and senior leadership to establish and grow a differentiated AI/ML services practice.

Key Responsibilities1. Practice Strategy Growth

- Define and execute the AI/ML practice vision, strategy, roadmap, and growth plan.
- Identify priority service offerings, industry focus areas, and high-value AI use cases.
- Build and scale capabilities across Machine Learning, Generative AI, LLMs, Agentic AI, NLP, Computer Vision, Predictive Analytics, and Enterprise AI.
- Develop differentiated AI offerings, reusable frameworks, accelerators, and intellectual property.
- Define annual business plans, investment priorities, capability development plans, and practice KPIs.
- Identify emerging technologies and evaluate their relevance for enterprise adoption and business value.

1. Revenue, PL Operational Ownership

- Own practice-level revenue, profitability, gross margin, utilization, resource planning, and forecasting.
- Build and maintain AI/ML pipeline and revenue forecasts in partnership with Sales.
- Monitor project margins, utilization, bench strength, hiring requirements, and delivery efficiency.
- Balance innovation investments with sustainable growth and profitability.
- Establish measurable KPIs for revenue growth, delivery performance, customer satisfaction, and capability maturity.

1. Business Development Go-to-Market

- Own AI/ML practice growth in partnership with Sales, including pipeline creation, solutioning, account expansion, and opportunity conversion.
- Develop market-facing AI/ML offerings and industry-specific propositions.
- Lead client discovery workshops, solution presentations, RFP/RFI responses, proposals, and strategic pitches.
- Identify and pursue current-logo and account expansion opportunities.
- Build repeatable GTM models around enterprise AI use cases.
- Support commercial discussions and help define value-based propositions and business cases.

1. Client Strategic Account Engagement

- Engage with CXOs, technology leaders, and business stakeholders to identify business challenges that can be addressed through AI.
- Act as a senior technology and business advisor for strategic clients.
- Build long-term relationships with key stakeholders and expand AI-led engagements within existing accounts.
- Translate business problems into practical AI roadmaps and measurable outcomes.




- Ensure AI initiatives move beyond PoCs into production adoption and business impact.

1. AI/ML Technology Solution Leadership

- Provide strategic and architectural direction for enterprise AI/ML programs.
- Guide solution design across Generative AI, LLMs, RAG, AI Agents, Agentic workflows, NLP, Computer Vision, Predictive Analytics, and traditional ML.
- Evaluate models, frameworks, platforms, orchestration approaches, vector/search technologies, and cloud AI services.
- Guide build-vs-buy and model-selection decisions based on business value, cost, scalability, performance, security, and maintainability.
- Review architectures and challenge technical decisions where required.
- Ensure solutions meet enterprise requirements around scalability, reliability, privacy, security, observability, and governance.

1. AI Economics Value Realization

- Evaluate the economics and business viability of AI solutions.
- Guide teams on inference cost, API/model cost optimization, infrastructure choices, and operational efficiency.
- Define measurable success metrics and ROI for AI initiatives.
- Ensure technology choices are aligned with business outcomes and sustainable operating costs.

1. MLOps, LLMOps Responsible AI

- Establish strong MLOps and LLMOps practices across deployment, evaluation, monitoring, CI/CD, observability, and model lifecycle management.
- Define standards for AI quality, model evaluation, traceability, governance, and auditability.
- Ensure appropriate controls around Responsible AI, data privacy, security, bias, model risk, and compliance.
- Establish production-readiness and governance frameworks for enterprise AI solutions.

1. Delivery Excellence Governance

- Provide executive oversight across major AI/ML programs and engagements.
- Establish delivery governance, technical review mechanisms, and risk-management practices.
- Ensure projects are delivered within timelines, budgets, quality standards, and client expectations.
- Review project health, delivery risks, technical escalations, and client issues.
- Support teams in taking solutions from experimentation and PoC to enterprise-scale production.

1. People Capability Building

- Build and lead high-performing teams across AI/ML Engineering, Data Science, ML Engineering, GenAI Engineering, Architecture, Consulting, and Pre-Sales.
- Define competency frameworks and career paths across the AI/ML practice.
- Build a strong leadership pipeline across architecture, delivery, and consulting.
- Drive technical training, certification, knowledge sharing, and internal AI communities.
- Participate in hiring and workforce planning aligned to practice growth.

1. Partner Technology Ecosystem

- Build relationships with hyperscalers, AI platform providers, foundation-model providers, and technology partners.
- Explore joint GTM, co-selling, and solution-development opportunities.
- Evaluate strategic technology partnerships that can accelerate practice growth and differentiation.

Required Skills Experience




- 15+ years of overall experience in technology, AI/ML, Data Science, or related areas.
- Significant experience in AI/ML leadership, consulting, delivery, or practice management.
- Demonstrated experience in building or scaling AI/ML capabilities or services.
- Strong understanding of Machine Learning, Deep Learning, NLP, Generative AI, LLMs, and enterprise AI architectures.
- Strong understanding of RAG, vector/search technologies, prompt engineering, fine-tuning, agentic systems, and AI orchestration.
- Good understanding of modern AI/ML engineering ecosystems, frameworks, and cloud AI platforms.
- Strong understanding of MLOps/LLMOps, deployment, monitoring, evaluation, observability, and governance.
- Experience designing and delivering enterprise-scale AI/ML solutions.
- Strong understanding of data engineering, data platforms, APIs, microservices, and distributed systems.
- Strong solutioning, architecture, technical problem-solving, and client-facing skills.
- Experience managing senior technical teams and complex programs.
- Robust commercial acumen with exposure to revenue growth, account expansion, forecasting, utilization, and profitability.

Preferred Qualifications
- Experience building or scaling an AI/ML practice from the ground up.
- Demonstrated experience owning or influencing a significant AI/ML services portfolio.
- Experience in AI consulting, pre-sales, RFP/RFI, account farming, and business development.
- Experience working with global enterprise customers.
- Experience developing AI accelerators, frameworks, reusable solutions, or intellectual property.
- Strong understanding of Responsible AI, AI governance, security, privacy, and model evaluation.
- Experience with major cloud and AI ecosystems such as AWS, Azure, GCP, Databricks, Snowflake, or equivalent platforms.
- Masters degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field is preferred.

Success Measures The AI/ML Practice Head will be measured on:
- AI/ML practice revenue growth and profitability.
- Pipeline creation and prospect conversion.
- Growth of strategic accounts and new client acquisition.
- Gross margin, utilization, and resource efficiency.
- Number and quality of production AI engagements.
- Customer satisfaction and measurable business outcomes.
- Development of reusable AI offerings, accelerators, and intellectual property.
- AI/ML capability maturity and leadership depth.
- Successful transition of AI initiatives from PoC to scaled production deployment.

Ideal Candidate Profile We are looking for a leader who can operate effectively across:

Business Growth + AI/Technology Leadership + Practice Delivery Excellence The ideal candidate should have demonstrated experience in:

Building AI capabilities Creating market-ready offerings Winning business Delivering enterprise AI solutions Growing accounts Scaling the practice profitably.

The role requires a leader who understands AI deeply but is equally focused on business outcomes, revenue growth, client value, and building a scalable AI/ML services organization .

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

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