Engagement Manager (8-17 yrs)

The Engagement Manager is responsible for ensuring the successful delivery of AI/ML and technology consulting projects, managing both client expectations and internal teams. They lead programs, guide strategic decision-making, and ensure alignment between client needs and project execution. This role requires a mix of business acumen, technical expertise, and strong leadership.

Key Responsibilities

Client Relationship Management:

- Serve as the main point of contact between the client and the organization.

- Build and maintain long-term client relationships by understanding their business goals and providing solutions through AI/ML technologies.

- Set clear expectations for project scope, deliverables, and timelines.

- Regularly engage with senior stakeholders to ensure satisfaction, identify potential business opportunities, and mitigate risks.

Program Leadership:

- Lead large-scale programs from inception to delivery, ensuring alignment with client objectives and timelines.

- Define program roadmaps, manage scope, budget, and timelines, and oversee multiple projects.

- Coordinate cross-functional teams, including data scientists, AI/ML engineers, software developers, and consultants.

- Implement program governance structures and regularly review progress with clients and internal teams.

Technical Consulting:

- Provide strategic technology advice to clients, especially on AI/ML implementation, system architecture, and integration with existing business processes.

- Conduct assessments to identify opportunities for automation, optimization, and AI-driven innovation within the client's business.

- Collaborate with internal experts to design and propose AI/ML models, ensuring they meet business objectives.

- Stay current with emerging technologies in AI/ML, cloud computing, and data analytics, and recommend how these can be leveraged by clients.

AI/ML Project Delivery:

- Oversee the design, development, and deployment of AI/ML models and solutions.

- Ensure AI/ML solutions are scalable, reliable, and provide measurable value to clients (e.g., increased efficiency, cost reduction, better decision-making).

- Collaborate with data science and engineering teams to ensure the technical feasibility of AI/ML solutions.

- Monitor performance metrics of AI/ML systems, ensuring they meet client expectations and regulatory standards.

Team Leadership and Mentoring:

- Lead, mentor, and coach a team of consultants, engineers, and data scientists, fostering a collaborative and innovative work environment.

- Provide technical and strategic guidance to the team, helping them navigate complex AI/ML projects.

- Ensure continuous learning and development within the team, encouraging upskilling in areas like AI, ML, and cloud technologies.

Risk Management and Problem Solving:

- Identify and mitigate risks throughout the program lifecycle, from design to deployment and post-launch.

- Troubleshoot issues related to project delivery, such as data availability, model accuracy, and integration challenges.

- Proactively manage project changes, re-aligning client expectations and internal efforts as necessary.

Performance Tracking and Reporting:

- Define key performance indicators (KPIs) and success metrics for both program execution and AI/ML model performance.

- Provide regular updates and reports to clients and internal leadership on project progress, risks, and business impacts.

- Use data analytics to demonstrate the ROI of AI/ML solutions delivered to clients.

Pre-sales and Business Development Support:

- Work with the sales team to identify new business opportunities and develop proposals, especially for AI/ML consulting projects.

- Assist in the development of Statements of Work (SOW), RFPs, and client presentations.

- Participate in client meetings, presentations, and workshops to secure new engagements.

Essential Skills and Competencies:

Program Leadership:

- Proven experience leading large, complex technology programs (preferably in AI/ML).

- Strong understanding of project management methodologies (Agile, Waterfall, etc.).

- Excellent organizational and multitasking skills.

Technical Expertise in AI/ML:

- Deep understanding of AI/ML principles, algorithms, and techniques, such as supervised and unsupervised learning, NLP, computer vision, and reinforcement learning.

- Hands-on experience with AI/ML tools and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).

- Familiarity with data management, cloud technologies (AWS, Azure, GCP), and large-scale data pipelines.

Tech Consulting:

- Strong business acumen and ability to translate client needs into technology solutions.

- Ability to align AI/ML solutions with the client's business goals and existing technical infrastructure.

- Experience conducting technology assessments and creating technology roadmaps for clients.

Communication and Stakeholder Management:

- Excellent communication skills, both verbal and written, with the ability to explain complex technical concepts to non-technical stakeholders.

- Proven ability to manage client relationships at all levels, from operational teams to C-suite executives.

Team Management and Leadership:

- Experience leading cross-functional teams in a dynamic, fast-paced environment.

- Strong interpersonal skills, with the ability to mentor, coach, and motivate team members.

- Proven track record of fostering a collaborative and high-performance work environment.

Problem-Solving and Analytical Thinking:

- Strong analytical and problem-solving skills, with the ability to address complex issues in AI/ML program delivery.

- Ability to anticipate challenges and implement proactive solutions.

Risk and Change Management:

- Ability to identify risks and potential roadblocks in AI/ML projects and implement mitigation strategies.

- Experience managing changes in project scope, re-aligning teams and resources accordingly.

Client-Focused Mindset:

- Strong focus on delivering value and ensuring client satisfaction.

- Ability to work with clients to shape project goals and define success metrics.

Desirable Qualifications:

- Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or related fields.

- Certification in AI/ML, data science, or cloud platforms (e.g., AWS Certified Machine Learning, Google Cloud AI/ML Engineer).

- PMP, PRINCE2, or Agile certifications.

- 8+ years of experience in program management, tech consulting, and AI/ML solutions delivery.

Industries:

- This role can be found in various industries, such as:

- Technology Consulting

- Healthcare (AI-driven diagnostics or data analysis)

- Finance (AI/ML for risk management and financial forecasting)

- Retail & E-commerce (Customer engagement, predictive analytics)

- Telecommunications (AI-driven network optimization)

- An Engagement Manager in AI/ML with a strong consulting and program leadership background bridges the gap between business needs and technical execution, ensuring both client satisfaction and the successful implementation of cutting-edge AI technologies.