Role Description BlackStone eIT is looking for a skilled AI Engineer to join our innovative team. As an AI Engineer, you will design, develop, and implement artificial intelligence solutions that address complex business challenges. You will work closely with data scientists, engineers, and stakeholders to build scalable AI models and integrate them into product offerings. This position offers a great opportunity to contribute to cutting-edge AI projects and help drive the adoption of AI technologies within our organization. You will be involved in all stages of AI project development, including: - Data collection and preprocessing - Model training - Deployment - Monitoring Qualifications - 3+ years of experience in AI or machine learning engineering roles - Proficiency in programming languages such as Python - Experience with AI/ML libraries like TensorFlow, PyTorch, or Scikit-learn - Knowledge of AI model deployment and monitoring best practices - Understanding of data processing, feature engineering, and model evaluation techniques - Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field - Strong problem-solving abilities and excellent communication skills - Experience with cloud computing platforms (AWS, Azure, or GCP) is preferred - Familiarity with containerization tools such as Docker is a plus Benefits - 💵 Competitive salary paid in USD - 🏡 Fully remote – work from anywhere - 🌎 Work with a diverse international team - 🚀 Exciting projects and a supportive engineering culture - 📈 Opportunities to grow technically and professionally

Role Description We are seeking a skilled and innovative AI Engineer who has experience working with Generative AI (GenAI) models, such as Large Language Models (LLMs), and implementing these solutions into web applications. This role combines the responsibilities of a full-stack developer with advanced knowledge of machine learning models, APIs, and cloud infrastructure, focusing on integrating state-of-the-art AI capabilities into user-friendly and efficient web-based applications. Key Activities: - Model Integration & API Development: - Integrate LLMs and other GenAI models into web applications through efficient API design and implementation. - Build and optimize API endpoints to allow seamless, real-time interaction between front-end applications and back-end AI models. - Design and develop secure, scalable, and high-performing microservices for AI model deployment. - Back-End Development & AI Pipelines: - Develop robust back-end systems in Python to support the deployment, scalability, and maintenance of GenAI models. - Build and maintain data pipelines, including preprocessing data and post-processing AI model outputs for consumption by applications. - Implement best practices for handling sensitive data and maintaining model performance. - Infrastructure & Deployment: - Use Kubernetes and Docker for containerization and orchestration to ensure scalable deployment and management of AI applications. - Implement continuous integration and continuous deployment (CI/CD) pipelines for automated testing and deployment of code changes. - Maintain a scalable and secure cloud infrastructure, leveraging platforms such as Google Cloud Platform, or Azure for model training, storage, and deployment. - LLM and GenAI Ecosystem Expertise: - Utilize vector databases (e.g., Pinecone, Weaviate, or Faiss) to manage and retrieve embeddings for efficient similarity search and recommendation systems. - Work with frameworks for model development and deployment, including Hugging Face Transformers, LangChain, and OpenAI. - Optimize and fine-tune LLMs to improve performance based on application needs. Qualifications - Min Bachelor's degree in Computer Science, Engineering, or a related field. - 7+ years of experience as a full-stack developer, ideally with a focus on AI model integration. - Proficiency in Python. - Strong knowledge of GenAI models and LLMs, including experience with model selection, tuning, and embedding strategies. - Experience with API development and integration to facilitate communication between front-end applications and AI models. - Working knowledge of containerization technologies like Docker and container orchestration with Kubernetes. - Familiarity with cloud platforms (AWS, GCP, or Azure) for AI model deployment and scalability. - Proficient in vector databases and their integration with LLM models for enhanced application functionality. - Familiarity with database management (SQL, NoSQL) and caching solutions (e.g., Redis). - Experience in CI/CD pipelines, code versioning (Git), and DevOps practices. - Excellent knowledge of English AND Polish. Preferred Qualifications - Knowledge of streaming architectures for real-time data processing and ingestion (e.g., Apache Kafka). - Familiarity with serverless architectures (e.g., AWS Lambda) for scalable deployment of AI features. - Prior experience in ML frameworks like TensorFlow, PyTorch, or ONNX. - Understanding of data privacy and security best practices in AI applications. Soft Skills - Strong problem-solving skills and ability to work independently and as part of a team. - Excellent communication skills to translate technical requirements into actionable development tasks. - Proactive approach to staying updated with emerging AI technologies and frameworks. Benefits - This is a unique opportunity to work on cutting-edge AI applications that leverage the latest advancements in Generative AI and Large Language Models, impacting the future of intelligent software development. - The option to work from the office or 100% remotely. - Opportunity to work in a diverse, international and cross-functional environment, along with leading experts. - Fulfilling careers with a range of benefits for people and invests in providing training opportunities for their development. - Involvement in technology monitoring and choices. - Your impact will be visible instantly and you will be making a difference in our users' lives. - B2B type of contract.

Title: AI Engineer - FDE (Forward Deployed Engineer) - U.S. Federal Sector Location: Washington, D.C., Maryland, or Virginia Job Description: PLEASE NOTE: Due to federal contract requirements and client site access obligations, U.S. citizenship and eligibility for a U.S. government secret clearance are required to access classified information. The position is based in the Washington, D.C., Maryland, or Virginia metropolitan area and includes periodic on‑site work and client collaboration. Candidates with an active Secret or higher clearance are strongly encouraged to apply. The AI Forward Deployed Engineering (AI FDE) team is a highly specialized customer-facing AI team at Databricks. We deliver professional services engagements to help our customers build and productionize first-of-its-kind AI applications. We work cross-functionally to shape long-term strategic priorities and initiatives alongside engineering, product, and developer relations, as well as support internal subject matter expert (SME) teams. We view our team as an ensemble: we look for individuals with strong, unique specializations to improve the overall strength of the team. This team is the right fit for you if you love working with customers, teammates, and fueling your curiosity for the latest trends in GenAI, LLMOps, and ML more broadly. This role can be remote. The impact you will have: - Develop cutting-edge GenAI solutions, incorporating the latest techniques from Databricks AI research to solve customer problems - Own production rollouts of consumer and internally facing GenAI applications - Serve as a trusted technical advisor to customers across a variety of domains - Present at conferences such as Data + AI Summit, recognized as a thought leader internally and externally - Collaborate cross-functionally with the product and engineering teams to influence priorities and shape the product roadmap What we look for: - Experience building GenAI applications, including RAG, multi-agent systems, Text2SQL, fine-tuning, etc., with tools such as HuggingFace, LangChain, and DSPy - Expertise in deploying production-grade GenAI applications, including evaluation and optimizations - Extensive years of hands-on industry data science experience, leveraging common machine learning and data science tools, i.e. pandas, scikit-learn, PyTorch, etc. - Experience building production-grade machine learning deployments on AWS, Azure, or GCP - Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience - Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike - Passion for collaboration, life-long learning, and driving business value through AI - [Preferred] Experience using the Databricks Intelligence Platform and Apache Spark™ to process large-scale distributed datasets - Willing to travel once every 4-8 weeks to see customers (as needed) Pay Range Transparency Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. Local Pay Range $185,920—$255,640 USD

Role Description You’ll run a small group inside our R&D function whose mission is to validate new product bets quickly. We move from “is this worth building?” to a working prototype in weeks, not quarters. You set the research direction, decide what to build and how, and lead the team to get it in front of users and stakeholders fast. This is a builder-leader role: roughly hands-on coding alongside the team, with the rest spent on strategy, planning, and unblocking people. - Own the technical vision and execution for a portfolio of PoCs, from initial research through working prototype. - Lead a small engineering team — set direction, review work, grow people, and keep the group focused and motivated. - Run lightweight research: scope a problem, survey the landscape, form a hypothesis, and define what a successful prototype must prove. - Translate fuzzy ideas into concrete plans with clear milestones, then execute against them. - Build fullstack — backend services, frontends, data pipelines, and the glue between them. - Work AI-first: use AI tooling to accelerate development, and build products that embed LLMs, agents, retrieval, and related techniques where they create real value. - Make pragmatic build-vs-buy and “good enough for a PoC” calls, balancing speed against the parts that need to be solid. - Partner with product, design, and leadership to decide which bets graduate from prototype to product. Qualifications - Proven technical leadership — has led a team or owned a major workstream, set technical direction, made the call on what to build, and shipped through others. - Strong fullstack engineering with real AI-building depth — production experience across backend and frontend in at least two of C#, Python, and JavaScript/TypeScript. - Genuinely self-directed and motivated — thrives with ambiguity, defines the problem when no one hands it to them, and drives it to a result. Requirements - Cloud experience (AWS, Azure, or GCP) and enough DevOps to ship — CI/CD, containers, getting a prototype deployed, observable, and demo-able. - Working understanding of ETL/ELT, data warehouses, and lakehouse concepts — enough to design a sensible pipeline and reason about how data flows through a prototype. - Hands-on data engineering depth is a bonus, not a requirement. Nice to have - Experience taking a 0→1 product or prototype to real users or market validation. - A standout specialty (distributed systems, ML/AI, frontend architecture, or data engineering) on top of the breadth. - Background in a startup, R&D lab, or innovation team where speed and ownership were the norm. - Familiarity with experiment design, rapid user testing, or other cheap ways to validate an idea. Who you are You’re equally comfortable whiteboarding a research plan, writing the first prototype yourself, and coaching a teammate through theirs. You’d rather ship something rough and learn than polish something no one needs. You can hold the big picture and the implementation detail at the same time, and people want to follow you because you bring clarity and momentum. Benefits - Continuous learning and career growth opportunities - Professional training and English/Spanish language classes - Comprehensive medical insurance - Mental health support - Specialized benefits program with compensation for fitness activities, hobbies, pet care, and more - Flexible working hours - Inclusive and supportive culture