• Create solutions for customers: Architect and implement intelligent LLM-powered ETL pipelines from scratch to transform unstructured client data into concrete, structured solutions. • Build individual agents : Develop autonomous, multi-agent systems using state-of-the-art frameworks to automate complex, multi-step human business processes. • Keep the quality high : Design and maintain robust evaluation frameworks to systematically quantify AI performance and serve as the gatekeeper of quality for production systems. • Own the whole cycle : Build and deploy high-performance RAG systems and wrap the AI logic into performant, production-ready microservices (e.g., FastAPI). • Grow with your team : Actively engage in creative brainstorming sessions and technical design discussions with teammates to challenge assumptions, jointly solve problems, and grow the team's collective knowledge.

• Develop and maintain our full‑stack applications within a modern JavaScript/TypeScript ecosystem • Design and implement scalable backend services with Node.js and Nest.js • Implement high‑performance frontend solutions using React.js and Next.js • Design and optimize relational databases (PostgreSQL, MySQL, MS SQL) • Containerize and orchestrate services using Docker and Kubernetes • Develop and integrate REST APIs • Build and operate data pipelines, feature stores and ML workflows for our agentic systems • Develop, train and evaluate machine learning models for marketing use cases (attribution, targeting, forecasting, anomaly detection) • Implement retrieval‑augmented generation (RAG) pipelines (embeddings, vector stores, re‑ranking) • Set up and maintain LLM evaluation frameworks, prompt optimization and systematic quality benchmarks • Apply code harnesses (notably Claude Code and Codex) in the daily development process — from planning through implementation to code review • Collaborate closely with the founding team on the product architecture of agentic systems

Role Description At the intersection of Artificial Intelligence, industrial systems, and applied ML research, we are looking for a Senior Machine Learning Researcher to help design and validate novel ML approaches for understanding complex engineering systems from heterogeneous technical data. The project is an early-stage R&D initiative focused on transforming industrial system descriptions, technical documentation, diagrams, specifications, and system data into structured representations that can support downstream modeling, simulation, and automation workflows. The focus of the role has shifted toward a pure ML research profile rather than a primarily engineering / implementation profile. We are looking for someone who can: - Explore research directions - Design experiments - Benchmark model approaches - Validate what is technically feasible in an ambiguous R&D environment Qualifications - Strong background in machine learning research, applied ML, computer vision, multimodal learning, graph ML, representation learning, or related areas - Experience designing and running ML experiments, benchmarking models, and interpreting results - Hands-on experience with deep learning frameworks such as PyTorch or TensorFlow - Ability to work with ambiguous research questions and translate them into testable hypotheses - Experience reading research papers and adapting methods to practical problems - Strong Python skills and ability to build research prototypes - Comfortable working independently in an R&D / PoC environment with evolving requirements - Strong commercial experience with Python and modern AI/ML ecosystems Requirements - Experience with document understanding, diagram understanding, technical drawings, engineering schematics, or similar visual / structured data - Experience with knowledge graphs, graph neural networks, graph embeddings, or structured relationship modeling - Experience with vision transformers, VLMs, self-supervised learning, contrastive learning, or synthetic data generation - Experience with industrial, scientific, simulation, robotics, manufacturing, or physical-system data - Experience with multimodal data fusion, combining visual, textual, structured, or sensor-based data - Familiarity with GCP / Vertex AI or other cloud-based ML environments Benefits At Avenga, everyone matters. We provide equal opportunities in recruitment, career development, and leadership, regardless of race, ethnicity, gender identity, sexual orientation, disability, age, religion, or any other characteristic. We are committed to fostering a work environment where our diverse community of employees, candidates, and business partners actively shapes our growth. By bringing together people from different backgrounds and experiences, we build a workplace where everyone feels free to be themselves while honoring the boundaries of others.

Role Description Within the Forward Deployment Engineering (FDE) Unit, the Advisory AI Architect is a hands-on technical leader who designs and builds AI and GenAI solutions with customers. You will own key parts of the AI software stack, drive rapid proof-of-value sprints, and help move AI workloads toward stable production on Dell infrastructure, balancing speed, reliability, security, and responsible AI practices. You focus on fit-for-purpose architectures and enablement so customers can operate and extend their AI solutions after FDE Units disengage. Join us to do the best work of your career and make a profound social impact as an Advisory AI Architect on our Forward Deployment Engineering (FDE) Team in Remote - New England. What You’ll Achieve - Design scalable AI architectures for customer use cases in line with enterprise and security standards - Lead implementation of AI software components such as model serving, orchestration, retrieval, and integrations - Run time-bound proofs of value to demonstrate business outcomes quickly and clearly - Help migrate AI workloads between cloud, on-premises, and hybrid environments on Dell infrastructure - Enable customer engineers and platform teams to support and extend solutions after handover You Will: - Design, configure, and implement AI solution stacks, including model serving, orchestration, retrieval pipelines, vector stores, and related components - Select practical tools and patterns to avoid tooling sprawl and deliver working prototypes and PoVs tied to clear success measures - Lead PoV sprints (typically a few weeks): refine scope, build and iterate on solutions, and present outcomes and recommendations to stakeholders - Plan and execute cloud-to-on-prem and hybrid migration patterns for AI workloads onto Dell infrastructure, optimizing performance, reliability, and security - Take shared responsibility for production readiness and early production stability, including monitoring, performance optimization, and issue triage - Work closely with AI Data Specialists to define and consume AI-ready data pipelines that support performance and reliability - Collaborate with AI Strategists and Enterprise Architects to align technical scope with business outcomes and target-state architectures - Provide documentation, runbooks, and knowledge transfer to customer teams, and contribute to Dell AI Factory reference architectures and playbooks - Mentor other technical team members on AI architecture patterns, Dell AI infrastructure capabilities, and responsible AI practices Qualifications - Typically, 6+ years in enterprise architecture, AI/ML platforms, or systems engineering, delivering production workloads in large enterprises - Hands-on experience designing and implementing AI solution components such as model serving, orchestration, retrieval pipelines, vector stores, and integrations - Strong foundation in modern software engineering practices (e.g., CI/CD, automated testing, observability, DevOps/SRE, infrastructure-as-code) - Experience delivering and operating production AI or other large-scale distributed systems in enterprise environments - Demonstrated ownership of production systems, including stabilization and performance tuning in high-visibility contexts - Ability to explain complex technical trade-offs and architectures in clear language for both technical and non-technical stakeholders - Ability to travel to customer sites and Dell locations as needed, in line with business requirements and applicable laws and Dell policies Desirable Requirements - Bachelor’s degree in computer science, Engineering, Data Science, or a related field - Experience deploying AI solutions across cloud, on-premises, and hybrid environments - Familiarity with AI security, privacy, and governance and modern cloud / data / ML tooling (e.g., major public clouds, Kubernetes) - Hands-on experience with GenAI or RAG-style solutions and leading small technical teams in customer-facing projects Compensation Dell is committed to fair and equitable compensation practices. The Total Target Compensation range for this position is $286,450 - 370,700, which includes base salary and commissions. Company Description We believe that each of us has the power to make an impact. That’s why we put our team members at the center of everything we do. If you’re looking for an opportunity to grow your career with some of the best minds and most advanced tech in the industry, we’re looking for you. Dell Technologies is a unique family of businesses that helps individuals and organizations transform how they work, live and play. Join us to build a future that works for everyone because Progress Takes All of Us. Dell Technologies is committed to the principle of equal employment opportunity for all employees and to providing employees with a work environment free of discrimination and harassment.