Perform engineering design and technical analysis using CAD tools, ensure compliance with environmental regulations, and prepare technical reports while collaborating with project managers and supporting team initiatives on diverse environmental...

• Analyze LiDAR point clouds and imagery. • Identify and validate infrastructure objects. • Perform measurements and data verification. • Work with GIS datasets, project databases, and engineering templates. • Ensure data quality by following established QA/QC procedures. • Report data issues and inconsistencies identified during processing. • Collaborate with project managers and QA engineers to ensure timely delivery.

Role Description As an ML Engineer at NineTwoThree AI Studio, you will sit at the intersection of production-grade software engineering, advanced natural language processing, and client delivery. We build custom, high-impact AI systems for brands and startups across diverse industries (such as healthcare, logistics, and fintech). Instead of siloed academic research, this role demands a product-minded builder. You will: - Design, optimize, and deploy robust LLM applications, custom predictive analytics, and agentic workflows directly into our clients' software ecosystems. - Take absolute ownership of features from prototype to production. Technology Stack - Core Frameworks & Arch: Transformer models, modern LLM APIs (Anthropic Claude, OpenAI, AWS Bedrock, etc.), Open-Source LLMs. - Orchestration & Agentic Design: Experience designing LLM workflows, agentic systems, or retrieval pipelines using frameworks such as Langchain, LangGraph, LlamaIndex, or equivalent approaches. - Data & Search: Vector databases (Pinecone, pgvector, Milvus, Qdrant, etc.), SQL, and data engineering pipelines. - Traditional ML: Supervised and Unsupervised learning (Classification, Regression, Anomaly Detection). - Cloud & Infrastructure: AWS (Lambda, SageMaker, Bedrock, EC2) and modern DevOps/retraining pipelines. - Languages: Production-grade Python. Responsibilities - Architect & Build AI Features: Design and implement robust classical ML and generative AI solutions, striking the right balance between autonomous agentic architectures and deterministic pipelines. - Evaluate: Design and maintain evaluation frameworks to measure AI quality, reliability, safety, and business impact before and after deployment. - Integrate & Deploy: Partner closely with full-stack developers and DevOps to seamlessly integrate AI capabilities into client web and mobile applications using serverless architecture (e.g., AWS Lambda) or API endpoints. - Optimize for Production: Refine prompts, system instructions, and chunking strategies to balance accuracy, latency, token consumption, and data privacy. - Traditional Predictive Analytics: Clean and process unstructured or historical client data to train/fine-tune custom algorithms for specific business problems (such as forecasting, classification, or anomaly detection). - Collaborate & Communicate: Actively participate in client discovery sessions, translate ambiguous business requirements into viable technical scopes, and demo prototypes directly to stakeholder teams. - Maintain Engineering Excellence: Engage in constructive code reviews, implement rigorous validation patterns to test AI outputs, and contribute templates or runbooks to our internal AI knowledge base. Requirements - Proven Track Record: 3+ years of experience engineering software with a strong focus on machine learning and natural language processing. - LLM & Generative AI Mastery: In-depth understanding of modern LLM architectures, context window mechanics, semantic search techniques, and the limitations of generative systems. - Production experience: Experience building and operating production AI systems, including monitoring, evaluation, debugging, and iterative improvement. - Evaluation experience: Understanding of evaluation methodologies for LLM-based systems, including retrieval quality, hallucination detection, and task-specific performance measurement. - Python & SQL Proficiency: Exceptional Python coding skills and the ability to query, clean, and structure data efficiently. - Cloud Infrastructure: Hands-on experience deploying ML or API services within cloud ecosystems, preferably AWS. - Ownership: Comfortable taking ownership of ambiguous problems from initial discovery through production deployment and ongoing support. Product & Team Capabilities - Ambiguity to Execution: Ability to drop into a completely new industry vertical, understand its data constraints, and spin up a working proof-of-concept within a few weeks. - The "Product Engineer" Mindset: Passion for seeing things ship and understanding why something is being built from a business value standpoint, not just what is being built. - Communication: Fluent written and spoken English. Comfortable interacting with client stakeholders and breaking down technical workflows into clear concepts. - Adaptability: Eagerness to experiment with and evaluate fast-emerging AI development tools, models, and frameworks. - Education: Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field (or equivalent practical experience). Benefits - Annual paid vacation: 20 days off per year during the first 3 years, increasing to 25 days in later years. - Paid sick leave, 10 national holidays, and 2 company days off. - Well-being budget. - Maternity/paternity leave. - Reimbursement of expenses for professional development courses and certifications (up to 100% in agreement with Manager). - Hardware upon business needs. - Strong positive engineering culture, a tightly-knit team of professionals with a good sense of humor. What's The Process We value your time and ours and make the process fast and easy. Our interview process takes the following steps: - A short interview with the HR. - 2nd technical interview with ML Engineer and CTO (optional). - 3rd live-coding interview. - Offer.

Role Description Intetics Inc., a global technology company providing custom software application development, distributed professional teams, software product quality assessment, and “all-things-digital” solutions, is looking for a GIS Engineer. - LiDAR data processing to support the measurement and recording of pole and pole attachment heights from LiDAR data. - Analyze LiDAR point clouds and imagery. - Identify and validate infrastructure objects. - Perform measurements and data verification. - Work with GIS datasets, project databases, and engineering templates. - Ensure data quality by following established QA/QC procedures. - Report data issues and inconsistencies identified during processing. - Collaborate with project managers and QA engineers to ensure timely delivery. Qualifications - GIS, cartographic, cadastral or related specialty or experience working with GIS. - Good spatial reasoning and analytical skills. - Strong attention to detail. - Ability to work with technical documentation and follow project guidelines. - Basic written English to study the instructions. Requirements - Experience with utility, telecom, or power infrastructure projects. - Experience working with LiDAR data. - Experience with GIS or point cloud software (QGIS, Global Mapper, MicroStation, TerraScan, Spatix, etc.). Benefits - 100% remote collaboration with a professional international team. - Opportunity to shape workflows and influence multiple LiDAR-based projects.