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What You'll Do
Product & Stakeholder Partnership
- 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.
- Work with global and local market teams during rollouts to understand adoption, identify gaps, and continuously improve the solution.
- 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.
- Ensure appropriate documentation of AI system architecture, model usage, data flows, risks, evaluations, and operational controls.
- Embed responsible AI and security considerations throughout the product lifecycle rather than treating them as post-development activities GenAI Architecture & Technical Strategy
- Define the technical vision, architecture, and engineering strategy for enterprise GenAI products.
- 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.
- 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.
- Conduct architecture and design reviews and provide technical direction across multiple engineering teams and workstreams. AI Evaluation & Quality




- 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. LLMOps / MLOps & Production Engineering
- 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.
- Design AI systems for scalability, availability, reliability, latency, and cost efficiency. Delivery & Team Leadership
- 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.
- Own execution and delivery: planning, prioritization, agile ceremonies, timelines, and quality of outcomes.
- Provide technical direction, unblock teams, and ensure pragmatic, scalable solutions aligned with business needs. 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.
- Design and develop scalable backend services, REST APIs, microservices, and integrations required to support AI-powered pro

📌 Global AI/ML Engineer Senior Manager (Gurugram)
🏢 Boston Consulting Group
📍 Gurugram