• Lead the architecture and delivery of agentic AI solutions • Translate business problems into scalable AI architectures • Define AI governance, risk management and responsible AI controls • Advise senior client stakeholders on AI strategy, operating models and technology roadmaps • Lead, coach and develop diverse, cross-functional teams

Role Description - Design and integrate LLM-powered features, including conversational interfaces, AI agents, structured generation, and retrieval-augmented systems. - Build and maintain ML pipelines for prediction, anomaly detection, classification, and time-series analysis. - Develop backend APIs and services connecting data sources, models, and client-facing applications. - Work with structured and unstructured data across relational databases, data warehouses, and external APIs. - Optimize model performance and scalability for production environments, including monitoring and fine-tuning. - Collaborate with cross-functional teams (product, data, and engineering) to translate business requirements into technical solutions. - Ensure code quality, documentation, and best practices for deployment, testing, and maintainability. Qualifications - Strong experience with FastAPI (or equivalent async frameworks), including dependency injection, UV, Pydantic, and async/await patterns (including thread pool executors for blocking operations). - Solid understanding of REST API design, including multi-tenancy, pagination, filtering, JWT/OAuth2 authentication, and structured error handling. - Proficiency in SQLAlchemy (including async sessions), raw parameterized queries, schema design, and migrations. - Hands-on experience integrating multiple LLM providers (e.g., OpenAI, Anthropic, AWS Bedrock, Ollama, Google Gemini, Snowflake Cortex) using provider abstraction layers. - Experience with JSON response validation, markdown/code-block extraction, and fallback error handling (preferably using frameworks like Pydantic). - Knowledge of prompt engineering techniques, including context injection, temperature/token tuning, and confidence scoring. - Familiarity with embedding-based retrieval and similarity scoring. - Experience with production-grade agentic frameworks such as Pydantic AI (structured output generation, agents). - Strong experience with gradient boosting models (e.g., XGBoost, LightGBM), including GPU-accelerated training, hyperparameter tuning, and evaluation. - Expertise in segmentation, anomaly detection, and feature engineering on high-frequency sensor data. - Experience with train/test splits, feature engineering, model evaluation (R², MAE, etc.), and experiment tracking (e.g., MLflow). - Understanding of when to combine classical ML with LLM-based components (e.g., LLM-assisted labeling, embedding features in tree models). - Strong database knowledge, including complex schemas, JSONB, partitioned tables, row-level security, query optimization, and vector extensions (e.g., pgvector). - Familiarity with NoSQL databases like MongoDB and specialized databases such as Redis and Qdrant is a plus. - Experience with Snowflake (including Snowpark, Model Registry, and Cortex) or equivalent platforms. - Hands-on experience with AWS services such as Bedrock, ECS, and EC2. - Experience with Docker and CI/CD pipelines. - Familiarity with S3 or equivalent object storage solutions. - Ability to work within VPN-gated infrastructure. - Experience across multiple client environments or industries (consulting background preferred). - Exposure to Industrial IoT or sensor data (high-frequency telemetry, signal processing). - Experience in NL-to-SQL or text-to-query system design. - Ability to handle multilingual data and implement internationalization. Requirements - Strong experience with FastAPI (or equivalent async frameworks). - Solid understanding of REST API design. - Proficiency in SQLAlchemy. - Hands-on experience integrating multiple LLM providers. - Experience with JSON response validation. - Knowledge of prompt engineering techniques. - Familiarity with embedding-based retrieval. - Experience with production-grade agentic frameworks. - Strong experience with gradient boosting models. - Expertise in segmentation and anomaly detection. - Experience with train/test splits and model evaluation. - Strong database knowledge. - Familiarity with NoSQL databases. - Experience with Snowflake or equivalent platforms. - Hands-on experience with AWS services. - Experience with Docker and CI/CD pipelines. - Familiarity with S3 or equivalent object storage solutions. - Ability to work within VPN-gated infrastructure. - Experience across multiple client environments. - Exposure to Industrial IoT or sensor data. - Experience in NL-to-SQL or text-to-query system design. - Ability to handle multilingual data.

Vantor is forging the new frontier of spatial intelligence, helping decision makers and operators navigate what’s happening now and shape what’s coming next. Vantor is a place for problem solvers, changemakers, and go-getters—where people are working together to help our customers see the world differently, and in doing so, be seen differently. Come be part of a mission, not just a job, where you can: Shape your own future, build the next big thing, and change the world. To be eligible for this position, you must be a U.S. Person, defined as a U.S. citizen, permanent resident, Asylee, or Refugee. Export Control/ITAR: Certain roles may be subject to U.S. export control laws, requiring U.S. person status as defined by 8 U.S.C. 1324b(a)(3). Please review the job details below. Team Overview: Insights is redefining how the world understands what’s happening on Earth. We are an AI-native, agentic intelligence platform built from the ground up to operate at planetary scale – transforming vast streams of geospatial data into predictive signals that matter. Powered by cutting-edge Google Cloud infrastructure, frontier Gen AI models, and collaboration with Google Research on next-generation Earth AI, we are pushing the boundaries of how AI can help enterprise see, reason about, and act on. Our mission is clear: deliver decision superiority in moments that matter. From national security to global enterprise operations, our platform provides contextual spatial awareness and anticipatory threat detection for customers operating in high-stakes environments. This is not incremental AI – this is intelligence engineered for global impact. Responsibilities - Design, develop, and deploy AI-native software products that transform large-scale geospatial data into actionable intelligence and mission-critical insights. - Implement multi-agent workflows using modern orchestration frameworks (e.g., Google ADK, LangChain, LangGraph) to enable autonomous reasoning, planning, and execution. - Integrate state-of-the-art Large Language Models (GPT-4, Claude, Gemini, etc.) to power contextual analysis, hypothesis generation, and adaptive decision-making. - Engineer agents capable of dynamic tool use, structured reasoning, and iterative self-refinement to improve insight quality over time. - Develop robust data ingestion and transformation layers to support pattern-of-life, detection, anomaly identification, and predictive analytics - Ensure secure, scalable integrations across cloud and enterprise environments. - Create feedback loops and reinforcement mechanisms to iteratively improve model reliability and operational trustworthiness. - Deploy and operate AI systems in production using modern DevOps practices (containerization, orchestration, CI/CD). - Leverage AI development agents (e.g., Codex, Gemini CLI, Claude Code) as force multipliers for software design, implementation, testing, and documentation. - Contribute to shared engineering standards, documentation, and best practices for AI-first development. Minimum Qualifications - Bachelor's degree in Computer Science, Systems Engineering, Software Engineering, or a related field. Advanced degrees are preferred but not required. - Must be a U.S. Citizen - 3+ years of proven experience in a relevant technology domain, with preference for Software engineer and AI/ML - ‘Strong understanding of technical concepts related to managing cloud-based data and machine learning pipelines (e.g., AWS or GCP) - Significant software development experience with Python, JavaScript, or similar Preferred Skills - Familiarity with vector databases, embeddings, and retrieval-augmented generation(RAG) architectures. - Knowledge of distributed systems design and high-availability architectures supporting global-scale workloads. - Leverages AI for team velocity and upskilling, including using AI-assisted development heavily, prototyping quickly, automating repetitive engineering tasks and moving faster than traditional SWE teams. - Excellent communication and interpersonal skills - Ability to work independently and collaboratively with remote and/or geographically distributed teams - Ability to work effectively in a fast-paced, dynamic environment and manage multiple priorities simultaneously Pay Transparency: In support of pay transparency at Vantor, we disclose salary ranges on all U.S. job postings. The successful candidate’s starting pay will fall within the salary range provided below and is determined based on job-related factors, including, but not limited to, the experience, qualifications, knowledge, skills, geographic work location, and market conditions. Candidates with the minimum necessary experience, qualifications, knowledge, and skillsets for the position should not expect to receive the upper end of the pay range. ● The base pay for this position within Colorado is: $124,000.00 - $206,000.00 annually.● The base pay for this position within New Jersey is: $124,000.00 - $206,000.00 annually.● The base pay for this position within Delaware is: $124,000.00 - $206,000.00 annually. ● The base pay for this position within the Washington, DC metropolitan area is: $137,000.00 - $228,000.00 annually.● The base pay for this position within California is: $143,000.00 - $209,000.00 annually.For all other states, we use geographic cost of labor as an input to develop market-driven ranges for our roles, and as such, each location where we hire may have a different range. Benefits: Vantor offers a competitive total rewards package that goes beyond the standard, including a robust 401(k) with company match, mental health resources, and unique perks like student loan repayment assistance, adoption reimbursement and pet insurance to support all aspects of your life. You can find more information on our benefits at: https://www.Vantor.com/careers The application window is three days from the date the job is posted and will remain posted until a qualified candidate has been identified for hire. If the job is reposted regardless of reason, it will remain posted three days from the date the job is reposted and will remain reposted until a qualified candidate has been identified for hire. The date of posting can be found on Vantor's Career page at the top of each job posting. To apply, submit your application via Vantor's Career page. EEO Policy: Vantor is an equal opportunity employer committed to an inclusive workplace. We believe in fostering an environment where all team members feel respected, valued, and encouraged to share their ideas. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, gender identity, sexual orientation, disability, protected veteran status, age, or any other characteristic protected by law.

• Develop technical source material for multiple sequential learning modules including code examples, system architecture references, case studies, assessment briefs, and worked solutions • Collaborate with learning designers to translate curriculum frameworks into technically accurate and production-relevant learning content • Participate in collaborative curriculum design sessions with academic contributors to refine programme scope, sequencing, and exit standards • Review learning materials throughout the development process to ensure technical accuracy, credibility, and alignment with current industry practices • Design summative assessments that reflect real-world AI engineering hiring and evaluation formats • Provide practitioner insight into the design of production AI application architectures and engineering workflows • Contribute expert insights to live masterclass or knowledge-sharing sessions following programme launch • Advise on the practical realities of building and deploying production AI applications, including common failure modes and operational challenges • Support the development of content covering evaluation methodologies, reliability patterns, governance considerations, and architectural decision-making for AI applications