Global AI/ML Engineer Senior Manager

Role overview

Global AI/ML Engineer Senior Manager at BCG

You will work closely with the People function & AI CoE leadership, Team Builder Product Owner, Tech owner, AI engineers, data scientists and stakeholders across the People function.

The role requires a combination of deep GenAI technical expertise, strong software engineering fundamentals, product thinking, delivery leadership, and the ability to influence senior technical and business stakeholders.

For this role, you will generally be expected to work from a BCG office or in person with clients on a regular basis (typically around 50% of working time), depending on business needs and in line with local policies and practices.

Responsibilities

• Work with global and local market teams during rollouts to understand adoption, identify gaps, and continuously improve the solution.
• Ensure appropriate documentation of AI system architecture, model usage, data flows, risks, evaluations, and operational controls.
• Design and guide implementation of LLM-powered applications, RAG architectures, agentic workflows, LLM orchestration, tool/function calling, and AI-powered decision-support solutions.
• Evaluate and select appropriate foundation models, embedding models, AI platforms, retrieval technologies, orchestration frameworks, and cloud services based on business and technical requirements.
• Conduct architecture and design reviews and provide technical direction across multiple engineering teams and workstreams.
• Design AI systems for scalability, availability, reliability, latency, and cost efficiency.
• Lead and coach a team of GenAI/ML engineers and data scientists delivering enterprise AI solutions end to end—from problem definition and design through development, deployment, and rollout.
• Provide technical direction, unblock teams, and ensure pragmatic, scalable solutions aligned with business needs.
• Design and develop scalable backend services, REST APIs, microservices, and integrations required to support AI-powered products.
• Work effectively across frontend technologies such as React/TypeScript or equivalent frameworks, and backend technologies such as Python/Java or equivalent technologies.
• Work effectively with modern cloud-native development stacks (e.g., GitHub-based workflows, Airflow, containerized/Kubernetes-based deployments, data pipelines).
• You’ll partner with product functional and technical teams to ensure successful delivery and global adoption of AI solutions.

Employment details

• Location: Gurgaon [Haryana] [India]

Application details

• Remain hands-on in Python-based GenAI/ML development while providing technical direction across the broader application stack.

Additional role details

• Partner closely with product owners, functional leaders, and stakeholders within the People function to define requirements, shape backlog items, and translate business problems into technical solutions.
• Partner with enterprise teams across architecture, security, Responsible AI etc. to seek approvals for the AI solutions during deployments and rollouts.
• Communicate clearly and effectively with both technical and non-technical stakeholders.
• Ensure GenAI solutions are designed and delivered in accordance with Responsible AI, security, privacy, data governance, and regulatory requirements.
• Establish appropriate AI guardrails covering areas such as prompt injection, data leakage, unauthorized tool access, unsafe outputs, and inappropriate model behaviour.
• Partner with Responsible AI, Security, Privacy, Legal, Risk, and Enterprise Architecture teams to obtain necessary approvals and establish production-readiness standards.
• Embed responsible AI and security considerations throughout the product lifecycle rather than treating them as post-development activities.
• Define the technical vision, architecture, and engineering strategy for enterprise GenAI products.
• Establish reusable GenAI architecture patterns, reference architectures, engineering standards, and technical accelerators across the AI CoE.
• Make pragmatic build-vs-buy, model-selection, and platform decisions considering quality, scalability, security, latency, and cost.
• Define evaluation frameworks for GenAI applications covering accuracy, relevance, groundedness, hallucination, safety, robustness, latency, and cost.
• Establish automated evaluation and regression-testing strategies for models, prompts, RAG pipelines, and agentic workflows.
• Partner with Product and business stakeholders to define AI quality and product success metrics.
• Establish production-grade LLMOps/MLOps practices covering model and prompt versioning, deployment, monitoring, observability, and continuous improvement.
• Define operational metrics covering AI quality, latency, reliability, usage, and cost.
• Establish CI/CD and automated testing practices for GenAI applications, prompts, RAG pipelines, and agentic workflows.
• Delivery & Team Leadership.
• Own execution and delivery: planning, prioritization, agile ceremonies, timelines, and quality of outcomes.
• Hands-on Technical & Full-Stack Engineering.
• Remain hands-on in full-stack development, solution design, code reviews, and technical architecture.
• Guide development of production-grade GenAI-enabled applications across the frontend, backend, AI/ML, and data layers.
• Guide development of modern frontend applications and user experiences that integrate GenAI capabilities into enterprise workflows.
• Guide integration of LLMs, AI agents, enterprise APIs, business systems, and data platforms into end-to-end product experiences.
• Work effectively with modern cloud-native development stacks, including GitHub-based workflows, containers/Kubernetes, Airflow, CI/CD, observability, and data pipelines.
• Promote modern software engineering practices across full-stack, AI, and ML development.
• Remain hands-on in Python-based development, code reviews, and solution design.
• Guide development of GenAI-enabled features and applied ML solutions in a product context.
• Promote modern software engineering practices across AI/ML development. 10+ years of experience in software engineering, ML/AI engineering, or data science, including significant experience designing and delivering production-grade GenAI/AI products and leading engineering teams.
• Hands on engineering development expertise to build and deploy production grade digital products.
• Demonstrated ability to lead delivery teams and grow into broader leadership responsibilities.
• Strong problem-solving skills with a pragmatic, outcomes-oriented approach.
• Proven experience leading teams of data scientists, ML engineers, or GenAI engineers delivering enterprise AI or digital products for internal or commercial products.
• Strong delivery and execution mindset, with experience owning timelines, dependencies, and outcomes.
• Excellent communication and stakeholder management skills, with the ability to engage product owners, functional leaders, and senior stakeholders.
• Strong hands-on experience with: Python /Applied AI / ML / GenAI in production environments.
• Cloud-based development and deployment environments.
• Data pipelines and production-grade systems.
• Ability to identify the right technical approach to solve business problems and guide teams accordingly.
• The Senior Manager will lead a multidisciplinary team of GenAI engineers, ML engineers, and data scientists while remaining hands-on in architecture, solution design, Python development, technical reviews, and production engineering.