Expert Consultant, Coro, AI Engineer

Tech Economy is a top-ranked consulting firm recognized for its exceptional workplace environment. They are seeking an AI Engineer to join the Coro team, focusing on building innovative AI-powered tools and software solutions to enhance B2B commercial excellence.

Responsibilities:

• Build AI-powered tools and products that drive real business outcomes
• Design and develop GenAI applications (e.g., copilots, workflow automation, decision support for commercial teams) 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 and 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 ML solutions end-to-end: data preparation, feature engineering, model selection, training, validation and 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 versus post-training concepts (e.g., instruction tuning and preference optimization approaches)
• Write clean, testable, maintainable code and ship AI services through the full SDLC: build, test, deploy, monitor, and 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 and policy optimization
• Design for secure enterprise deployment: access controls, auditability, data handling for sensitive and PII data, and responsible AI guardrails
• Build reusable components and accelerators (templates, evaluation harnesses, connectors, orchestration patterns) that scale across client contexts
• 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