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minfy

Posted 31 Aug 2026

AI Engineer

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Employment
Full time
Team
Artificial Intelligence (AI)
Posted
31 Aug 2026

About the role

We are hiring an AI Engineer to serve as a  technical contributor across multiple customer engagements. On each engagement you will set the technical direction, make the key modeling decisions, and stay hands-on throughout, acting as a senior technical point of contact with the customer, explaining trade-offs, managing expectations, and turning results into clear business recommendations and outcomes. Across engagements you will help build the reusable assets, patterns, and technical standards that raise the bar for a scaling AI engineering organization.What You’ll Do• Own the technical strategy on customer engagements, making the architecture and modeling decisions and being accountable for the results.• Ramp quickly into unfamiliar domains and problem types, scoping the right approach for each customer's data, constraints, and timeline.• Stay hands-on: build the pipelines, train and evaluate the models, run the experiments, and write the critical code.• Set the technical bar and support other engineers through design reviews, mentorship,and pairing.• Act as a senior technical point of contact with customers, communicating progress,risks, and results to both engineers and senior stakeholders, and managing expectationsthrough ambiguity.• Engineer features and datasets from large-scale customer data, and integrate signals into ML model training and runs.• Design, build, and evaluate LLM and agentic solutions including prompt and context design, retrieval, tool use, multi-step agent workflows, and orchestration.• Design and run structured, parallel experiments that measure the gains from GenAI approaches over strong conventional ML methods.• Own model and agent development end to end, including feature integration, hyperparameter optimization, and error analysis.• Define and run the evaluation framework including task-level quality metrics, LLM and agent evaluation harnesses, and ablation studies.• Establish the path to production: model and agent serving, latency and cost management, shadow-mode testing, A/B framework readiness, and guardrail metrics.• Build reusable accelerators, reference architectures, and internal standards that carry from one engagement to the next.• Deliver clear technical documentation and lead knowledge-transfer sessions so each customer's teams can operate and iterate independently after handoff.Required Qualifications• 10+ years in applied machine learning / data science, with deep hands-on experience inbuilding and shipping production ML systems across multiple problem domains.• Hands-on experience with LLMs in production: prompt and context engineering, retrieval-augmented generation, embeddings, and reasoning about evaluation, latency, and cost.• Hands-on experience building agentic systems including tool use and function calling, multi-step workflows, orchestration frameworks, and agent evaluation and guardrails.• Experience with Amazon Bedrock or comparable managed LLM platforms.• Strong communication skills, able to explain modeling decisions, trade-offs, and resultsclearly to engineers, data scientists, and senior business stakeholders, and to manageexpectations through ambiguity.• Customer-facing or stakeholder-facing experience: building trust, navigating competing priorities, and serving as a senior technical voice in high-stakes conversations.• Comfort working across several engagements and customer contexts at once, switching domains without losing technical rigor.• A track record of technical leadership through mentoring engineers, driving designdecisions, and setting standards.• Strong track record taking models from experimentation to production, owning the offline-to-online validation story (evaluation metrics, ablations, shadow testing, A/B readiness).• Deep, hands-on expertise in deep learning and embedding-based architectures with a major framework (PyTorch or TensorFlow).• Strong feature engineering on large datasets using the modern data stack (Spark, SQL, distributed data lakes).• Rigorous experimental methodology including hyperparameter optimization and a disciplined, hypothesis-driven approach to measuring true lift.• Hands-on AWS experience across the ML lifecycle, and strong proficiency in Python.• Experience in MLOps and LLMOps including model and prompt versioning, monitoring, evaluation pipelines, and reproducible training.Preferred Qualifications• Prior experience in a client-facing consulting or professional-services delivery environment.• Advanced degree in Computer Science, Machine Learning, Statistics, or a related quantitative field.

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