• Design, train, evaluate, and ship machine learning and deep learning models (classification, regression, ranking, vision, NLP, time series) against well-defined business and scientific problems. • Design and implement LLM-powered applications using major model providers (Claude/Anthropic, Azure OpenAI, OpenAI, or equivalents). • Build retrieval-augmented generation (RAG) systems, including chunking strategies, embeddings, vector store selection (e.g., Azure AI Search, pgvector), and re-ranking. • Develop agentic workflows with tool use and orchestration; implement guardrails, evaluation harnesses, and human-in-the-loop review where appropriate. • Apply prompt engineering, fine-tuning, and structured output techniques; measure quality with offline evals and online metrics. • Build and maintain robust data and feature pipelines on Azure; ensure data quality, lineage, and reproducibility. • Productionize models with sound MLOps practices: versioning, automated retraining, drift detection, and rollback strategies. • Use AI coding assistants (Claude Code, Cursor) effectively alongside traditional manual coding — choosing the right approach for each task and reviewing AI-generated code with rigor. • Manage source code in Git using a clean branching strategy and pull-request-based review. • Build and maintain CI/CD pipelines (e.g., Azure DevOps, GitHub Actions) for automated testing, packaging, and deployment of AI services. • Deploy and operate services on Azure using containers (Docker), orchestration (Kubernetes/AKS), and infrastructure-as-code (Terraform or Bicep). • Instrument AI systems with logging, tracing, and evaluation pipelines so that quality, latency, and cost can be observed and managed over time. • Partner with security and compliance to address data privacy, PHI/PII handling, prompt injection, and model risk in regulated contexts. • Work directly with product, scientific, and business stakeholders to scope problems, set realistic expectations, and choose the right level of AI sophistication for the job. • Mentor engineers on AI-assisted development practices and on the patterns and pitfalls of LLM-based systems. • Conduct rigorous design and code reviews; advocate for evaluation, safety, and observability as first-class concerns.

Role Description We invite a talented Senior AI Engineer with a genuine passion for AI and curiosity for exploring new technologies to join Dev.Pro. In this role, you’ll build AI solutions end-to-end — from experiments and prototypes to production — for internal teams and incoming projects. You’ll also contribute to the company’s AI growth by sharing knowledge, documenting best practices, and guiding teams in adopting AI. Qualifications - Power user of modern AI coding tools — agentic and spec-driven workflows - Hands-on GenAI experience with LLM APIs, prompt engineering, LangChain, n8n, and AI feature development - Testing and evaluation of GenAI systems (LLM evals), with solid code quality as a baseline - 8+ years in software engineering with strong Python and production backend experience - Passion for AI coding demonstrated via side projects, experimentation, and autonomous coding practices - Deep understanding of ML fundamentals, including implementing basic models and gradient descent from scratch - Strong SQL and practical experience with vector databases and embeddings - Able to own initiatives end-to-end and work autonomously - Strong analytical thinking and problem-solving skills - Expertise in Agile workflows, balancing R&D and delivery priorities - Excellent technical communication, documentation, and cross-disciplinary collaboration - Upper-Intermediate English level Requirements - Experience in ML tasks (feature engineering, imbalanced datasets, data prep) - Hands-on with visual AI workflows, model fine-tuning, agent orchestration, and evaluation - Exposure to data engineering (pipelines, transformations, warehouse / lake / lakehouse) - Architecture documentation using C4, ADR, or similar decision-capture patterns - Experience across JavaScript/Node.js and .NET - Cloud & deployment knowledge (any major cloud — AWS, GCP, or Azure) - R&D experience (PoCs, demos); client-facing / presales a plus Benefits - 99.9% remote — you can work from anywhere in the world - 30 paid days off per year to use however you like — vacations, holidays, or personal time - 5 paid sick days, up to 60 days of medical leave, and up to 6 paid days off per year for major family events like weddings, funerals, or the birth of a child - Partially covered health insurance after the probation, plus a wellness bonus for gym memberships, sports nutrition, and similar needs after 6 months - We pay in U.S. dollars and cover all approved overtime - Join English lessons and Dev.Pro University programs, and take part in fun online activities and team-building events Key Responsibilities - Design and develop AI solutions end-to-end, from experiments and prototypes to production-ready systems - Conduct R&D and build PoCs, exploring new AI tools and methods, testing ideas, and turning validated concepts into working solutions - Collaborate with cross-functional teams to design and integrate AI solutions effectively - Document solutions, experiments, and best practices to grow AI expertise across the company - Advise internal teams on AI adoption, tools, and implementation strategies - Lead or participate in internal workshops and knowledge-sharing sessions

• Own AI-powered features end-to-end, from scoping, design and requirements engineering to feature evaluation, deployment and operation. • Design, build and maintain cloud-native, event-driven AI applications across the full stack. • Evaluate, integrate, and optimize AI models for production use, balancing quality, latency, reliability and cost. • Collaborate effectively with cross-functional team members, including non-technical stakeholders, to refine requirements and align technical solutions with business objectives. • Build and maintain robust AI inference, evaluation, and monitoring pipelines. • Develop and implement robust, automated AI application evaluation frameworks. • Analyze AI application performance data and experiments, using insights for data-driven improvements. • Operate and improve production systems, taking ownership of reliability, quality, and technical debt. • Stay current with AI advancements and share knowledge to upskill the team. • Plan, track, and break down work effectively in an agile team. • Provide technical leadership, drive design reviews, write ADRs and contribute to hiring and onboarding. • Participate in on-call rotations, incident response and post-mortems for AI-powered systems and help define SLOs and error budgets.

• Serve as the engineering lead for Agentic AI delivery across Supply Planning and Manufacturing — owning the design, development, and deployment of production-grade AI agent solutions. • Architect and build multi-agent AI systems using Azure AI Agent Service, AutoGen, Semantic Kernel, and/or LangChain/LangGraph — including orchestrator-executor patterns, tool calling, memory management, and agent coordination. • Implement the MCP to surface enterprise data as structured context for AI agents operating in supply chain and manufacturing workflows. • Build and deploy generative AI solutions on Azure AI Foundry — RAG-based knowledge agents, decision support for forecasting and capacity planning, and document intelligence for maintenance work orders and recipes. • Design and deliver AI copilots and topic-based agents using Microsoft Copilot Studio — enabling Supply Planning and Manufacturing teams to access insights and take action directly from Teams and Outlook. • Act as the AI delivery owner for agentic use cases — scoping business problems with stakeholders, defining agent capabilities and tool surfaces, prioritizing the roadmap, and driving adoption. • Apply emerging agentic AI patterns — including ReAct, Plan-and-Execute, reflection, and human-in-the-loop — for supply chain and operational use cases. • Partner with Supply Chain leadership, Demand Planning, Process Engineering, Maintenance Ops, and Plant teams to identify, scope, and deliver AI use cases that influence operational decisions. • Define and maintain AI agent governance — prompt versioning, tool auditing, evaluation frameworks, observability, and safety guardrails for production deployments. • Develop on Azure Databricks — PySpark and SQL against gold/platinum Delta tables, notebooks for transformation and feature work, and orchestration via Workflows. • Build and maintain Power BI reports and semantic models that serve as grounding data for AI agents and executive dashboards across Supply Planning and Manufacturing. • Own Supply Chain AI metrics alignment cadence — keeping priorities, status, and roadblocks visible to Supply Chain and Manufacturing leadership. • Mentor analysts and engineers on agentic AI design patterns, MCP, and AI delivery best practices.