# AI and Data Practice Lead **Company:** [PMC Commerce](https://jobs.workable.com/companies/gFJiW8ZqBjXCm9yWK4rZbX.md) **Location:** Vadodara, India **Workplace:** on site **Employment type:** Full-time **Department:** India [Apply for this job](https://jobs.workable.com/view/2c4ee421-1e38-40a7-af3e-757dada7742c) ## Description **Summary of the position** We are seeking an experienced and visionary **Practice Lead – AI & Data Engineering** to build, scale, and lead our AI and Data Engineering practice as a strategic capability within the organisation. The role will define the **technical vision, delivery standards, talent strategy, service offerings, and commercial roadmap** for the practice. While our current engagements include **Microsoft Fabric, Azure AI, GenAI and ML**, the role is technology-agnostic and expected to continuously evolve with emerging technologies, market trends, and client needs. This is a strategic leadership role combining **technical expertise, architecture governance, delivery excellence, people leadership, client advisory, and commercial acumen**. **Key accountabilities** **Practice Strategy & Vision** - Define and own the AI, ML and Data Engineering practice roadmap, including technology direction, service offerings, accelerators, and reusable frameworks. - Evaluate emerging cloud and open-source platforms including **Azure, AWS, Microsoft Fabric, Databricks, Snowflake**, and their AI/ML capabilities. - Establish the practice as a **Centre of Excellence (CoE)** for data engineering, analytics enablement, AI/ML development, deployment, and governance. - Develop reusable accelerators for data engineering and AI/ML use cases, including **RAG pipelines, prompt libraries, model templates, feature engineering, and MLOps/LLMOps patterns**. - Align practice strategy with business growth objectives, client needs, and evolving trends such as **GenAI and AI-driven decision-making**. **Technical Leadership & Architecture Governance** - Act as the technical authority across data engineering and AI/ML engagements. - Define reference architectures, engineering standards, and best practices covering: - Data lakes, lakehouses and data warehouses - ETL/ELT, batch, streaming and real-time pipelines - Data quality, observability, metadata, lineage and governance - Feature engineering, model development, serving and monitoring - Vector databases, RAG, MCP, LLM and agentic architecture patterns - MLOps/LLMOps, CI/CD, experiment tracking, model versioning and automated retraining - Responsible AI, model explainability, fairness and governance - Review solution designs to ensure **scalability, security, performance, maintainability, and reliability**. - Ensure strong alignment between data engineering, analytics, and AI/ML initiatives. **Delivery Excellence & Quality Assurance** - Provide technical oversight across multiple client engagements and ensure consistent delivery quality. - Define and enforce engineering practices covering **coding standards, code reviews, testing, CI/CD, documentation, and knowledge management**. - Conduct technical health checks and delivery audits to identify risks and improvement opportunities. - Partner with Delivery Managers and Architects to resolve complex technical and delivery challenges. **Client Engagement & Pre-Sales Support** - Act as a trusted technical advisor to clients on **data strategy, AI/ML adoption, modernisation, and platform selection**. - Support pre-sales activities including **solutioning, estimations, proposals, client presentations, feasibility assessments, and ROI evaluation**. - Translate business requirements into scalable data and AI/ML architectures and implementation approaches. - Help clients progress from traditional reporting towards **predictive, AI-augmented, and generative AI-driven decision-making**. Build and scale teams across **Data Engineering, ML Engineering, Data Science, MLOps, Architecture, and Technical Leadership**. Define skill matrices, learning paths, and certification plans across data and AI/ML technologies. Mentor senior engineers and data scientists and develop future practice leaders and architects. **Talent Development & Capability Building** Foster a culture of **engineering excellence, responsible AI, ownership, innovation, and continuous learning**. ## Requirements **Skills and Experience** - **Core Experience** - **12–15 years** of experience across AI, ML, Data Engineering, analytics platforms, or large-scale integration programmes. - **5+ years** in a senior technical leadership, architecture, or practice-building role. - Proven experience building and scaling **enterprise-grade data platforms and solutions**. - Experience working across multiple client engagements and managing competing priorities. - **Technical Expertise** - Strong hands-on and architectural experience with modern data and AI/ML platforms, including: - **Azure Data Factory and Microsoft Fabric** or equivalent platforms. - **Azure Machine Learning, Databricks/MLflow**, or equivalent ML platforms. - **Azure AI Foundry**, Azure OpenAI, AI Agents and exposure to Agentic Architecture. - ETL/ELT, streaming and event-driven data pipelines. - Data lakehouse and warehouse architectures. - Feature engineering, model training/serving and MLOps. - GenAI concepts including **LLMs, prompt engineering, RAG, MCP, vector databases, and agentic frameworks**. - AI productivity/tooling such as **Claude, Microsoft Copilot**, or similar. - Experience across cloud platforms; **Azure preferred**, with AWS/SageMaker exposure being an advantage. - DevOps, CI/CD and Infrastructure-as-Code for data and ML pipelines. - Working knowledge of **Responsible AI**, including explainability, bias/fairness, governance, and AI adoption within the SDLC. - **Leadership & Business Skills** - Strong stakeholder management and executive communication skills. - Ability to balance technical depth with business and commercial considerations. - Strong strategic thinking with a bias towards execution and measurable outcomes. - Ability to influence and lead across teams without relying solely on formal authority. - Strong client-facing and consultative approach. **Personal Attributes** - Passionate about building a **world-class, future-ready engineering practice**. - Technology-agnostic, curious, innovative, and forward-thinking. - Strong ownership and accountability for outcomes. - Entrepreneurial mindset with a focus on **value creation, scalability, and continuous improvement**. - Passionate about developing people and building a culture of technical excellence.