Director/Practice Lead - AI ML

Locations: Bangalore, Pune, Chennai, Hyderabad and Noida

Key Responsibilities:

1. Strategic Leadership:
1. Develop and execute a strategic roadmap for the data science and AI/ML practice, aligning with the firm's overall objectives and market trends.
2. Identify opportunities for innovation and differentiation, leveraging data science and AI/ML to solve complex business problems and create value for clients.
2. Client Engagement:
1. Engage with clients to understand their business objectives, challenges, and opportunities, and collaborate with them to design and deliver customized data science and AI/ML solutions.
2. Serve as a trusted advisor, providing expertise on industry best practices, emerging technologies, and innovative approaches to leveraging data for competitive advantage.
3. Team Management:
1. Lead and mentor a team of data scientists, machine learning engineers, data engineers, and AI specialists, fostering a culture of collaboration, innovation, and excellence.
2. Provide guidance on project scoping, resource allocation, and technical direction, ensuring successful project delivery and client satisfaction.
4. Solution Development:
1. Oversee the design, development, and implementation of data science and AI/ML solutions, ensuring alignment with client requirements, industry standards, and best practices.
2. Collaborate with cross-functional teams to integrate data science and AI/ML capabilities into existing systems and workflows, maximizing impact and value for clients.
5. Thought Leadership and Innovation:
1. Stay abreast of industry trends, emerging technologies, and best practices in data science and AI/ML, and drive innovation through research, experimentation, and knowledge sharing.
2. Contribute to thought leadership initiatives, such as whitepapers, blogs, and presentations, showcasing the firm's expertise and thought leadership in data-driven solutions.
6. Pre-sales Support:
1. Collaborate closely with the sales team to understand the specific requirements and pain points of our clients, and develop tailored proposals and solutions.
2. Conduct client meetings and presentations to articulate the value proposition of our AI/ML/Gen AI services in addressing the unique challenges faced by the key industry sectors.
3. Work with the sales team to craft responses to RFPs (Request for Proposals) and RFIs (Request for Information) with customized solutions.
7. Go-to-Market Strategy:
1. Develop and execute go-to-market strategies specifically targeting the industry verticals, identifying key stakeholders, industry trends, and competitive positioning opportunities.
2. Collaborate with the marketing team to create targeted collateral, case studies, and thought leadership content highlighting our AI?ML/Gen AI expertise.
3. Actively participate in industry events, conferences, and forums to promote our AI/ML/Gen AI services and establish thought leadership within the sector.

Qualifications:

• Advanced degree (Ph.D. or Master's) in Computer Science, Statistics, Mathematics, Engineering, or related field.
• Extensive experience (10-12+ years) in data science, machine learning, AI, or related domains, with a proven track record of leading successful projects and teams within a system integration context.
• Strong understanding of system integration principles, architectures, and technologies, with the ability to design and implement data science and AI/ML solutions that seamlessly integrate with existing systems and processes.
• Use Azure Machine Learning and Azure OpenAI to develop and deploy machine learning models.
• Hands-on experience with Azure Machine Learning, Azure OpenAI, Azure AI Search, and vector databases
• Knowledge of Azure App Services and Azure SQL databases.
• Implement solutions using Large Language Models (LLMs) for natural language processing and AI applications.
• Utilize Azure AI Search or vector database to enhance data retrieval and insights.
• Build scalable applications using Azure App Services and integrate them with Azure SQL databases.
• Apply prompt engineering techniques to fine-tune AI models for specific tasks.
• Experience in at least one of the industry verticals such as Fintech, Life sciences & Healthcare, Manufacturing, or energy & Utilities is MUST, along with relevant certifications in data science, AI, or related fields.
• Deep understanding of Generative AI and latest market trends and create a roadmap and vision for our clients.
• 4-5 years of experience in working as a data science practice leader at Big 4 or boutique consulting firms
• Excellent communication, leadership, and client-facing skills, with the ability to build trusted relationships, influence stakeholders, and articulate complex technical concepts to diverse audiences.
• Strong analytical and problem-solving abilities, with a passion for driving innovation and leveraging data-driven insights to solve business challenges for clients.
• Experience in solutions architecture. technical domains such as AI/ML, multimodal ML, model evaluation, MLOps, MLSecOps, ML training, inference, data engineering, data science, fine-tuning
• Manage and mentor a team of skilled data scientists, fostering a culture of collaboration, innovation, and continuous learning.
• Proficiency in programming languages such as Python, R, Java, along with experience with data science and AI/ML libraries and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
• Take the lead in designing the AI architecture and selecting technologies from both open-source and commercial offerings.
• Knowing the workflow and pipeline architectures of ML and deep learning workloads, including the components and trade-offs across data management, governance, model building, deployment, and production workflows, is crucial.
• Experience in advanced analytics tools (Python, R) along with applied mathematics, ML, Deep Learning frameworks (such as TensorFlow), and ML techniques (such as random forest and neural networks).
• Experience in Machine Learning solutions (using various models, such as Linear/Logistic Regression, Support Vector Machines, Deep Neural Networks,..)
• Developing AI and ML models in real-world environments, and integrating AI and ML using cloud-native or hybrid technologies into large-scale enterprise applications.
• Experience in developing best practices for the ML life cycle, feature engineering, model management, MLOps, deployment, and monitoring.