The Role

We are hiring an AI Engineer to build GenAI and agentic AI applications for enterprise use cases, ranging from rapid proofs of concept (POCs) to MVPs and, where appropriate, scaled production deployments. You will design and implement LLM-driven applications and agentic workflows that use tools, data, and enterprise systems to execute multi-step tasks reliably and safely. This is a hands-on individual contributor role with growing influence on technical direction and an opportunity to begin mentoring more junior team members.

While GenAI and agentic AI are the primary focus, you will also draw on data science and ML engineering skills as needed, including building evaluation approaches, working with data pipelines, and developing or integrating ML models when they materially improve performance or reliability.

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

- Design and develop GenAI applications (e.g., copilots, workflow automation, decision support) using modern LLM stacks.
- Implement agentic workflows where they add clear value (e.g., tool use, multi-step execution, human-in-the-loop controls), with attention to reliability, safety, and clear failure modes.
- Design and build advanced search, retrieval,



and knowledge pipelines across diverse data structures and stores (e.g., hybrid search, vector stores, graph databases/knowledge graphs, and traditional data platforms), covering indexing strategies, metadata design, relevance tuning/reranking, freshness, caching, access controls, and source attribution.
- Build robust agent capabilities including context engineering, memory/state management (short-term and long-term), orchestration, routing, and tool integration patterns.
- Integrate solutions into enterprise environments and workflows (APIs, data systems, collaboration tools), balancing quality, latency, cost, privacy, and adoption.
- Translate ambiguous client needs into explicit technical requirements, tradeoffs, and delivery plans.

Build and apply data science and machine learning capabilities

- Build ML solutions end-to-end: data preparation, feature engineering, model selection, training, validation/testing, and performance analysis.
- Apply the right methods for the problem, spanning classical ML and deep learning (including sequence, text, and image models when relevant).
- Create reproducible training and evaluation pipelines (versioning, experiment tracking, robust validation, clear documentation).
- Demonstrate fluency with modern deep learning concepts, including transformer fundamentals and LLM pre-training vs post-training concepts (e.g.,



instruction tuning and preference optimization approaches).

Engineer for real delivery: POC MVP production

- Write clean, testable, maintainable code and ship AI services through the full SDLC: build test deploy monitor iterate.
- Implement MLOps and GenAIOps practices: CI/CD, reproducibility, environment parity, model/prompt/agent versioning, and operational readiness.
- Build evaluation and observability for GenAI and agentic systems: tracing and instrumentation, regression test suites, automated scoring where appropriate, and iteration loops for prompt/policy optimization.
- Design for secure enterprise deployment: access controls, auditability, data handling for sensitive/PII data, and responsible AI guardrails.
- Build reusable components and accelerators (templates, evaluation harnesses, connectors, orchestration patterns) that scale across client contexts.

Thrive in a client-facing consulting environment

- Communicate clearly with technical and non-technical stakeholders; lead working sessions, present recommendations, and write crisp technical documentation.
- Work effectively with Bain consultants to prioritize the critical few technical decisions that unlock business value.
- Support proposal shaping and scoping: effort sizing, architecture options, risk assessment, and delivery roadmaps.

What We're Looking For

- Core engineering + AI application skills
- 3-5+ years of professional AI / ML

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Lead, AI Engineering (Mumbai)
🏢 Bain
📍 Mumbai