Lead AI Engineering

Bain & Company; Gurugram District; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Bain & Company, through its AIS team, leverages AI to design and deliver enterprise-grade AI solutions. The Lead AI Engineer role focuses on hands-on development, end-to-end generative AI systems, and scalable deployment in consulting settings.

You will collaborate with product managers, data scientists, and engineers, contribute to multi-agent pipelines, and mentor junior colleagues while exploring partnerships with AI ecosystem players.

Qualifications

• 3–5+ years in software engineering, ML engineering, or applied AI roles with hands-on building responsibilities.
• Demonstrated experience shipping generative AI features or systems end-to-end, from prototyping through production.
• Clear communication skills with the ability to explain technical concepts to non-technical collaborators and stakeholders.
• Demonstrated ability to collaborate effectively across engineering, product, and data science teams; mentoring peers is a plus.
• Strong prompt engineering and context engineering skills; familiarity with conversation design principles.
• Working knowledge of evaluation design, experimentation frameworks, and data labeling strategies for LLM applications.
• Experience with RAG architectures (vector-based retrieval; hybrid/graph approaches a plus), agentic patterns (tool use, routing, memory management), ReAct, RLAIF, HITL + feedback loops.
• Familiarity with AI-specific tools & frameworks - orchestration, vector/graph databases, and Model+API ecosystems.

Responsibilities

• Contribute to the design, development, and deployment of end-to-end generative AI systems, including multi-agent workflows and production-grade AI applications.
• Build and iterate on multi-component AI pipelines, including RAG, fine-tuning, embedding generation, and hybrid retrieval strategies.
• Implement reasoning, tool use, function calling, and orchestration across AI workflows.
• Build and contribute to agentic systems with sound engineering practices for separation of concerns, memory architecture, and tool integration.
• Contribute across the full stack: model experimentation, evaluation design, and production system deployment.
• Build and maintain APIs, microservices, CI/CD pipelines, and cloud-native deployments with observability and reliability.
• Support GenAIOps processes for automated testing, regression evaluation, latency monitoring, and continual improvement.
• Balance performance, safety, responsible AI, and cost; implement guardrails, HITL workflows, and ethics alignment.

Skills

Generative AI features

Cross-functional collaboration

Prompt engineering

System design

Evaluation design

LLM workloads

Client-facing communication

Mentoring peers

Tools

Orchestration frameworks

Vector databases

Graph databases

Model+API ecosystems

Job description

Job Description:

What Makes Us a Great Place to Work

We are proud to be consistently recognized as one of the world’s best places to work. We are currently the top ranked consulting firm on Glassdoor’s Best Places to Work list and have earned the #1 overall spot a record seven times. Extraordinary teams are at the heart of our business strategy, but these don’t happen by chance. They require intentional focus on bringing together a broad set of backgrounds, cultures, experiences, perspectives, and skills in a supportive and inclusive work environment. We hire people with exceptional talent and create an environment in which every individual can thrive professionally and personally.

About Bain AI, Insights & Solutions (AIS)

Bain’s AI, Insights & Solutions (AIS) team works with clients to design and deliver AI-powered solutions that create measurable business impact. You’ll operate in multidisciplinary teams alongside Bain consultants, other experts in product, design, architecture and engineering, and client stakeholders, translating ambiguous business problems into robust AI applications that can be piloted, scaled, and adopted.

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.

The Role

The Lead AI Engineer will design, build, and ship generative AI systems and agentic solutions for Bain’s clients. You will contribute across the full development lifecycle — from early experimentation and prototyping through to production deployment — collaborating closely with senior engineers, product managers, and data scientists. This is a hands-on individual contributor role with growing influence on technical direction and an opportunity to begin mentoring more junior team members.

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 by doing, with support from experienced teammates, frequent feedback, and increasing responsibility over time.

What You’ll Do

• Contribute to the design, development, and deployment of end-to‑end generative AI systems, including multi‑agent workflows and production‑grade AI applications.
• Build and iterate on multi‑component AI pipelines, including:
  • Retrieval‑Augmented Generation (RAG)
  • Fine‑tuning and parameter‑efficient tuning
  • Embedding generation and optimization
  • Hybrid retrieval strategies (vector, graph, keyword)
• Implement reasoning, tool use, function calling, and orchestration across AI workflows
• Build and contribute to agentic systems, applying sound engineering principles around separation of concerns, memory architecture, and tool integration
• Contribute across the full stack: model experimentation, evaluation design, and production system deployment
• Build and maintain APIs, microservices, CI/CD pipelines, and cloud‑native deployments with attention to observability and reliability
• Support and help build GenAIOps processes for automated testing, regression evaluation, latency monitoring, and continual improvement
• Balance performance, safety, responsible AI principles, and cost across system design:
  • Implement guardrails, fallbacks, red‑team​ing strategies, and human‑in‑the‑loop (HITL) workflows
  • 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:
  • Hallucination rate and factual consistency
  • Relevance and precision/recall
  • Latency, throughput, and system‑level performance
  • Cost tracking and efficiency
• Partner closely with product, engineering, data science, ethics, and infrastructure teams to build robust, compliant AI systems
• Contribute technical insights and communicate findings clearly to cross‑functional stakeholders and, where relevant, clients
• Share knowledge with peers and support a culture of technical learning around RAG, agents, prompt engineering, and AI safety

What We’re Looking For (Qualifications)

• 3–5+ yearsin software engineering, ML engineering, or applied AI roles with hands‑on building responsibilities
• Demonstrated experience shipping generative AI features or systems end‑to‑end, from prototyping through production
• Clear communication skills with the ability to explain technical concepts to non‑technical collaborators and stakeholders
• Demonstrated ability to collaborate effectively across engineering, product, and data science teams; some experience supporting or informally mentoring peers is a plus
• Solid prompt engineering and context engineering skills; familiarity with conversation design principles
• Working knowledge of evaluation design, experimentation frameworks, and data labeling strategies for LLM applications
• Experience with:
  • RAG architectures (vector‑based retrieval; exposure to hybrid or graph‑based approaches is a plus)
  • Agentic patterns (tool use, routing, memory management; multi‑agent systems experience is a plus)
  • ReAct, RLAIF, and other HITL + feedback loops.
  • AI‑Specific Tools & Frameworks - Orchestration frameworks, Vector and graph databases and Model + API ecosystems
• Strong background in system design, architecture, and production‑grade deployment
• Familiarity with cost and latency tradeoffs when working with LLM workloads
• Comfort operating in high‑ambiguity environments with collaborative cross‑functional teams
• Eagerness to grow into a technical leadership role and support junior team members
• Experience in client‑facing consulting or enterprise transformation environments is a strong plus

Working Model & Travel

• This role requires a minimum of three days per week working together in person, either at a client location or at your Bain home office.
• Travel is required beyond your home office / primary working location. Frequency and destination vary by project needs.