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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. While GenAI and agentic AI are the primary focus, you will also draw on data science and ML engineering skills as needed.

What You’ll Do

- Build AI applications that drive real business outcomes
- 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 (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 clear 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 (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 (Qualifications)

- Core engineering + AI application skills
- 3–5+ years of professional AI / ML engineering experience (or equivalent), with strong backend engineering fundamentals.
- Strong proficiency in Python and experience building APIs/services (REST/gRPC) and integrating with enterprise systems.
- Hands‑on experience building LLM-powered applications with delivery considerations (latency, cost, reliability, security).
- Experience building advanced retrieval/search systems (hybrid retrieval, vector search, reranking) and comfort working across multiple data stores (vector, graph,



relational/document/search).
- Experience implementing agentic patterns (context management, tool integration, orchestration, memory/state handling) and strong judgment about when agentic approaches are (and are not) appropriate.
- Strong engineering practices: testing, code review, version control, CI/CD, and performance profiling.

- Cloud, platform, and production delivery experience

- Experience deploying and operating services on AWS, GCP, and/or Azure (environment management, reliability, observability, scaling).
- Experience with Docker and Kubernetes (or equivalent orchestration) and operating services in production (debugging, performance, resilience).
- Proven ability to implement security, privacy, and governance requirements for AI systems (authentication/authorization, access controls, PII/sensitive data handling, enterprise risk controls).

- Breadth of knowledge across data science and machine learning

- Experience training, validating, and testing ML models; strong understanding of overfitting, generalization, and evaluation methodology.
- Practical experience with feature engineering and data preprocessing for real-world datasets.
- Familiarity with a broad set of ML algorithms (classic ML and deep learning) and ability to choose methods that match the business and data constraints.
- Familiarity with deep learning frameworks (PyTorch/TensorFlow) and ML lifecycle tooling (experiment tracking, model registry, feature store concepts).

- Delivery mindset and consulting skills

- Proven ability to operate in ambiguity and complexity, manage priorities, and deliver outcomes independently or with a collaborative team.
- Excellent interpersonal and communication skills, able to explain technical decisions, tradeoffs, and results to mixed audiences.
- Strong stakeholder management skills; comfort working directly with clients.

- Language Requirements

- English is mandatory.
- Spanish/Portuguese is a 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.

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Postúlate en Kit Empleo: kitempleo.cl/empleo/1dokm1

📌 Lead, AI Engineering (Santiago)
🏢 BainInc
📍 Santiago