Expert Sr Manager, AI Engineering

Bain; New Delhi; Sur place; INR 4 200 000 - 6 000 000; Plein temps

Résumé du poste

Bain seeks an experienced AI Engineering leader to architect, build, and scale next-generation generative AI systems for client engagements. You will bridge advanced engineering, applied AI research, product strategy, and responsible AI governance, owning the lifecycle from research to production deployment and ongoing optimization.

You will collaborate with Bain teams and external partners, shaping enterprise AI adoption, ensuring safety, scalability, and value delivery while mentoring

Qualifications

• 8-12+ years in software engineering, ML engineering, or applied AI roles with significant hands-on building responsibilities.
• Demonstrated experience leading complex, multi-stack generative AI programs from conception through production.
• Strong executive communication skills with ability to translate technical concepts to business stakeholders.
• Track record of leading engineering teams and mentoring technical talent.

Responsabilités

• Design, build, and deploy end-to-end generative AI systems, including multi-agent workflows and production-grade AI applications.
• Architect multi-component pipelines (RAG, fine-tuning, embedding, hybrid retrieval).
• Integrate reasoning, tool use, function calling, and orchestration across complex workflows.
• Lead research, model experimentation, and production deployment across teams.
• Oversee API development, CI/CD, observability, and cloud-native deployment.
• Build automated evaluation suites and robust AI system governance.

Connaissances

Generative AI

Multi-agent systems

Prompt engineering

AI safety

Architectural design

Production deployment

RAG architectures

Executive communication

Outils

Orchestration frameworks

Vector/graph databases

Description du poste

The Impact You'll Have

Bain works with clients on board-level and executive priorities, helping deliver step-change results across growth, productivity, and resilience. In that context, AI is rarely a point solution. The most meaningful outcomes come from building AI as part of an integrated system that combines technology with redesigned processes, operating model changes, and adoption at scale across the organization.

As an AI Engineering leader in AIS, you will support the building of the technical core within these transformations and work as part of broader Bain consulting teams to move solutions from prototype to real adoption. The result is measurable impact at the company or enterprise level and, in many cases, helps clients set new performance standards for their industries.

The Role

The Expert Senior Manager AI Engineer will architect, build, and scale next-generation generative AI systems and agentic solutions for Bain's clients. As a leader in the practice, you will sit at the intersection of advanced engineering, applied AI research, product strategy, and responsible AI governance. You will own the full lifecycle - from research and experimentation to production deployment and ongoing optimization - and guide teams across engineering, product, data science, ethics, and client stakeholders.

You will have opportunities to work with major AI ecosystem partners through Bain's partnerships, collaborating on real client deployments and helping shape how emerging capabilities are applied in enterprise settings.

Bain offers significant learning and growth opportunities through the breadth and depth of problems we solve, the level of impact we help clients achieve, and our apprenticeship model. You will learn while doing, with support from other experienced teammates, frequent feedback, and increasing responsibility over time.

What You'll Do

• Design, build, and deploy end-to-end generative AI systems, including multi-agent workflows and production-grade AI applications.
• Architect multi-component pipelines, including:
  1. Retrieval-Augmented Generation (RAG)
  2. Fine-tuning and parameter-efficient tuning
  3. Embedding generation and optimization
  4. Hybrid retrieval strategies (vector, graph, keyword)
• Integrate reasoning, tool use, function calling, and orchestration across complex workflows
• Engineer advanced agentic systems, ensuring clear separation of concerns, robust memory architecture, and scalable tool ecosystems
• Lead everything from early-stage research, model experimentation, and evaluation design to production system deployment
• Oversee API development, microservices, CI/CD pipelines, observability, and cloud-native deployment
• Build scalable GenAIOps processes for automated testing, regression evaluation, latency monitoring, and continual improvement
• Balance performance, safety, responsible AI principles, and cost across system design:
  1. Implement guardrails, fallbacks, red-teaming strategies, and human-in-the-loop (HITL) workflows
  2. Partner with global ethics teams to ensure alignment with Bain's Responsible AI standards
• Build automated evaluation suites integrating user signals, continual learning cycles, and ongoing model updates
• Design and implement evaluation frameworks covering:
  1. Hallucination rate and factual consistency
  2. Relevance and precision/recall
  3. Latency, throughput, and system-level performance
  4. Cost tracking and efficiency
• Partner closely with product, engineering, data science, ethics, and infrastructure teams to build robust, compliant AI systems
• Act as a thought partner to executives and clients on AI strategy, architecture decisions, emerging capabilities, and implementation roadmaps
• Mentor and upskill technical teams on best practices including RAG, agents, prompt engineering, and AI safety

What We're Looking For

• 8-12+ years in software engineering, ML engineering, or applied AI roles with significant hands‑on building responsibilities
• Demonstrated experience leading complex, multi-stack generative AI programs from conception through production
• Strong executive communication skills with the ability to translate highly technical concepts to business stakeholders
• Track record of leading engineering teams, mentoring technical talent, and collaborating with diverse cross‑functional groups
• Advanced prompt engineering, context engineering, and conversation design
• Strong expertise in evaluation design, experimentation frameworks, and data labeling strategies for LLM apps
• Deep experience with:
  1. Advanced RAG architectures (vector, hybrid, graph-based retrieval)
  2. Agentic architectures (multi-agent systems, tool selection, routing, memory, planning, reflection)
  3. ReAct, RLAIF, and other HITL + feedback loops
  4. AI-Specific Tools Frameworks - Orchestration frameworks, Vector and graph databases and Model + API ecosystems
• Strong background in system design, architecture, and production‑grade deployment
• Deep familiarity with cost optimization and computational tradeoffs for LLM workloads
• Comfort operating in high‑ambiguity