Client Delivery Manager - Data Engineering & Analytics

Key Roles & Responsibilities:

- Own end-to-end delivery of software engineering and AI/ML engagements for enterprise clients, from solution design through deployment, monitoring, and support.

- Act as the primary technical point of contact for clients, engaging directly with business stakeholders to understand requirements and convert them into clear technical solutions and delivery plans.

- Lead architecture, design, and code reviews to ensure solutions follow strong software engineering practices, including scalability, security, and maintainability.

- Apply and guide the adoption of AI/ML and GenAI capabilities within client solutions, ensuring sound model design, evaluation, and MLOps practices where applicable.

- Manage project scope, timelines, budgets, and risks across multiple concurrent engagements, proactively escalating issues to leadership and clients as needed.

- Partner with client stakeholders, including business and technology leaders, to identify new opportunities and shape ongoing solution roadmaps.

- Track and report delivery KPIs, and communicate project status, technical decisions, and business outcomes clearly to both technical and non-technical audiences.

Required Qualifications & Skills:

- Bachelor's degree in Computer Science, Engineering, or a related field from a Tier-I or Tier-II institute; an advanced degree (MS/MTech) is a plus.

- 6 to 10 years of overall industry experience in software engineering, with meaningful exposure to data engineering, analytics, or AI/ML systems.

- Techno-functional profile with a deep, hands-on technical background, and the demonstrated ability to converse with business stakeholders and convert requirements into technical solutions.

- Strong grounding in software engineering practices, including system design, coding standards, testing, CI/CD, version control, and cloud-native architectures (AWS, Azure, or GCP).

- Practical experience building and deploying AI/ML and/or GenAI solutions, including traditional ML, NLP, and LLM-based applications, along with MLOps practices.

- Proficiency in Python and/or other modern programming languages, along with relevant frameworks and tools for data and AI engineering.

- Prior experience managing, mentoring, or leading engineering teams, with a track record of successful client delivery.

- Excellent communication and stakeholder management skills, with confidence operating in client-facing settings.

- Strong analytical and problem-solving abilities, with comfort navigating ambiguity in a fast-paced consulting environment.