B

Boston Consulting Group

Global management consulting and digital transformation

Global AI/ML Engineer Senior Manager

Full-TimePosted on 10/14/2026
No salary listed
Expert
Gurugram, Haryana, India
HybridGenerally expected to work from a BCG office or with clients around 50% of working time.
Software EngineeringAI Application Engineering, Full-Stack Engineering

About the job

Requirements
  • At least 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.
  • Proven experience leading teams of data scientists, ML engineers, or GenAI engineers delivering enterprise AI or digital products for internal or commercial products.
  • Experience owning timelines, dependencies, and outcomes.
  • Experience engaging product owners, functional leaders, and senior stakeholders.
  • Strong hands-on experience with Python, applied AI, machine learning, and generative AI in production environments.
  • Experience with cloud-based development and deployment environments.
  • Experience with data pipelines and production-grade systems.
  • Ability to identify the right technical approach to solve business problems and guide teams accordingly.
  • Generally expected to work from a BCG office or in person with clients around 50% of working time, depending on business needs and local policies.
Responsibilities
  • Partner with product owners, functional leaders, and People function stakeholders to define requirements, shape backlog items, and translate business problems into technical solutions.
  • Work with global and local market teams during rollouts to understand adoption, identify gaps, and continuously improve solutions.
  • Partner with enterprise architecture, security, and Responsible AI teams to secure approvals for AI solutions during deployments and rollouts.
  • Communicate with technical and non-technical stakeholders.
  • Ensure GenAI solutions meet Responsible AI, security, privacy, data governance, and regulatory requirements.
  • Establish AI guardrails for prompt injection, data leakage, unauthorized tool access, unsafe outputs, and inappropriate model behavior.
  • Partner with Responsible AI, Security, Privacy, Legal, Risk, and Enterprise Architecture teams to obtain approvals and establish production-readiness standards.
  • Document AI system architecture, model usage, data flows, risks, evaluations, and operational controls.
  • Embed responsible AI and security considerations throughout the product lifecycle.
  • Define the technical vision, architecture, and engineering strategy for enterprise GenAI products.
  • Design and guide implementation of LLM-powered applications, retrieval-augmented generation architectures, agentic workflows, LLM orchestration, tool/function calling, and AI-powered decision-support solutions.
  • Evaluate and select foundation models, embedding models, AI platforms, retrieval technologies, orchestration frameworks, and cloud services based on business and technical requirements.
  • Establish reusable GenAI architecture patterns, reference architectures, engineering standards, and technical accelerators across the AI Center of Excellence.
  • Make build-versus-buy, model-selection, and platform decisions based on quality, scalability, security, latency, and cost.
  • Conduct architecture and design reviews and provide technical direction across engineering teams and workstreams.
  • 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, retrieval-augmented generation pipelines, and agentic workflows.
  • Partner with Product and business stakeholders to define AI quality and product success metrics.
  • Establish production-grade LLMOps/MLOps practices for model and prompt versioning, deployment, monitoring, observability, and continuous improvement.
  • Define operational metrics for AI quality, latency, reliability, usage, and cost.
  • Establish continuous integration/continuous delivery and automated testing practices for GenAI applications, prompts, retrieval-augmented generation pipelines, and agentic workflows.
  • Design AI systems for scalability, availability, reliability, latency, and cost efficiency.
  • Lead and coach GenAI/ML engineers and data scientists delivering enterprise AI solutions end to end, from problem definition and design through development, deployment, and rollout.
  • Own execution and delivery, including planning, prioritization, agile ceremonies, timelines, and quality of outcomes.
  • Provide technical direction, unblock teams, and ensure pragmatic, scalable solutions aligned with business needs.
  • Remain hands-on in full-stack development, solution design, code reviews, and technical architecture.
  • Guide development of production-grade GenAI-enabled applications across frontend, backend, AI/ML, and data layers.
  • Design and develop scalable backend services, REST APIs, microservices, and integrations for AI-powered products.
  • Guide development of modern frontend applications and user experiences integrating GenAI capabilities into enterprise workflows.
  • Work across frontend technologies such as React/TypeScript or equivalent frameworks and backend technologies such as Python/Java or equivalent technologies.
  • Guide integration of LLMs, AI agents, enterprise APIs, business systems, and data platforms into end-to-end product experiences.
  • Remain hands-on in Python-based GenAI/ML development while providing technical direction across the broader application stack.
  • Work with 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.
  • Work with modern cloud-native development stacks, including GitHub-based workflows, Airflow, containerized/Kubernetes-based deployments, and data pipelines.
  • Promote modern software engineering practices across AI/ML development.
Desired Qualifications
  • No specific preferred or desirable qualifications are stated.

About the company

B

Boston Consulting Group

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BCG provides global management consulting services across strategy, operations, digital transformation and organizational change for businesses, governments, and nonprofits. Teams of consultants, data scientists and industry experts work with client leadership to design and implement solutions, often embedding specialists inside client organizations. A key differentiator is BCG X, a technology build-and-design division that blends management consulting with product engineering, design and venture-building to develop and deploy technology-enabled solutions. Compared with traditional firms, BCG combines strategic advisory with hands-on product development and execution, spanning both the private and public sectors. The firm's explicit goal is to help clients achieve lasting performance improvements and sustainable impact by guiding transformation initiatives from strategy through implementation and, where relevant, through creating new digital products or ventures.

Company Size

10,001+

Company Stage

N/A

Total Funding

N/A

Headquarters

Boston, Massachusetts

Founded

1963

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Simplify's Take

What believers are saying

  • BCG says corporations will double AI spending in 2026, boosting transformation demand.
  • BCG's 2026 Applied AI Index shows almost 50% of companies now generate AI value.
  • BCG careers still recruit globally in October 2026, signaling continued growth and selective hiring.

What critics are saying

  • Law360 reported an August 28, 2026 pregnancy bias settlement, exposing employer-brand and legal risk.
  • Automattic v. BCG and Rady v. BCG keep the firm defending active 2026 litigation.
  • If clients buy AI workflows directly, BCG's billable-hours model breaks by 2027.

What makes Boston Consulting Group unique

  • BCG X, led by Sylvain Duranton, sells strategy plus industrial-grade AI product engineering.
  • BCG's 2026 AI consulting emphasizes deploy, reshape, and invent, tied to outcomes.
  • BCG built Singapore's Future Ready Business Index with SBF, DBS, and EnterpriseSG.

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Benefits

  • Health Insurance
  • Dental Insurance
  • Vision Insurance
  • Paid Vacation
  • Paid Parental Leave
  • Family Planning Benefits
  • 401(k) Retirement Plan
  • Wellness Program

Company News

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Oct 1st, 2026
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