Role Description We are looking for a highly skilled Machine Learning Engineer with strong expertise in both traditional machine learning and modern Generative AI (LLMs) to design, build, and deploy scalable AI solutions. The ideal candidate will work on large-scale structured and unstructured data, enabling intelligent automation and insights, particularly in domains such as insurance (claims, underwriting, fraud detection). Qualifications - Strong foundation in machine learning algorithms (supervised & unsupervised) - Experience with anomaly detection, time-series, and predictive modeling - Proficiency in Python and ML libraries (Scikit-learn, XGBoost, PyTorch/TensorFlow) - Hands-on experience with LLMs (OpenAI, Claude, Hugging Face, etc.) Requirements - Strong understanding of RAG (Retrieval-Augmented Generation) - Prompt engineering & evaluation - Embeddings & vector databases (e.g., FAISS, Milvus) - Experience building GenAI applications (chatbots, document summarization systems) - Experience working with large datasets (batch + streaming) - Knowledge of data pipelines and tools (Spark, Airflow, or similar) - Familiarity with cloud platforms (Azure, AWS, or GCP) - Experience deploying models using APIs (FastAPI/Flask) - Understanding of Docker, CI/CD pipelines, and model monitoring - Knowledge of version control and experiment tracking tools Benefits - Lead large-scale AWS-to-GCP cloud transformation initiatives. - Work on enterprise Data Lakehouse and analytics modernization projects. - Opportunity to migrate enterprise BI platforms from Qlik Sense to Looker. - Flexible remote work environment. - Exposure to global enterprise customers. - Collaborative, innovation-driven engineering culture. - Continuous learning and certification opportunities.

Role Description We are looking for a Full Stack Engineer - AI Systems to build the product layer that turns these capabilities into usable, production-grade workflows. This includes designing how agents operate, fail, recover, and deliver consistent value to users. This position is 100% Remote. Full Stack Engineer Responsibilities: - Build end-to-end product features across frontend, backend, and AI integrations. - Design agent workflows that handle planning, tool use, failure, and recovery across multiple steps. - Integrate LLMs, memory, and external tools into systems that behave reliably under real-world conditions. - Design real-time AI interactions with streaming, partial results, and tight latency constraints. - Improve system reliability, observability, and fallback mechanisms. - Collaborate closely with ML, backend, and product teams to ship features end-to-end. - Continuously iterate based on real usage and failure modes. Full Stack Engineer Outcomes: - Own and ship AI-native product features that move beyond chat into persistent, goal-driven workflows. - Design and deploy agent workflows that reliably complete multi-step tasks across tools and sessions. - Reduce latency and improve responsiveness of AI interactions while maintaining output quality. - Build robust fallback and recovery mechanisms for LLM and tool failures in production environments. - Improve the success rate and reliability of AI-driven workflows through iteration, evaluation, and monitoring. - Establish patterns and abstractions for integrating LLMs, memory, and external tools into scalable product systems. - Contribute to a product experience where AI feels proactive, consistent, and dependable over time. Qualifications - Strong experience in Full Stack Engineering (Frontend and Backend). - Artificial Intelligence (AI) experience required. - Solid understanding of system design and API architecture. - Experience working with LLMs, RAG systems, or AI-powered applications. - Ability to handle ambiguity and make pragmatic engineering decisions. - Strong ownership; able to take features from idea to production. - Comfort working in fast-moving environments with evolving requirements. - Tech Stack: AI, Anthropic, API, Artificial Intelligence, Backend, Docker, Frontend, Kubernetes, Next.js, Node.js, NoSQL, OpenAI, Claude, Open Source, LLM, Python, Pytorch, RAG Systems, and SQL. Benefits - Medical insurance - Dental - Vision - Savings Plan Options - PTO Compensation USD 140,000 - USD 190,000 yearly

• Technically oversee the architecture to identify, describe, and enrich events in historical vehicle logs using Multimodal LLMs. • Oversee the off-board ingestion and fusion of semantic scene descriptions, ego-centric kinematics, and internal autonomy telemetry to create a holistic diagnostic context for LLM inference. • Develop structured prompting templates utilizing Contextual Prompting (CP), Chain-of-Thought (CoT), and In-Context Learning (ICL) to evaluate scenarios. • Architect the integration of foundation models into the Metrics Engine (ME), designing efficient cascade filtering and log slice parallelization strategies to scale high-volume LLM inference across simulation and on-road drive logs while managing computational latency and costs. • Define, design, and implement key metrics to evaluate autonomous vehicle performance, such as lane change capability, oscillations, and braking. • Deploy and manage a Retrieval-Augmented Generation (RAG) vector database containing codified AV Driving Policies to ground off-board LLM evaluations in specific Operational Design Domains. • Serve as a technical escalation point and collaborate with Autonomy (Planner, Prediction, Perception) and Systems teams to deliver high-signal, enriched events. • Drive the transition toward Direct Vector-LLM Fusion utilizing emerging Physical AI ecosystems and open-weights Vision-Language-Action (VLA) models to process telemetry off-board without text-translation bottlenecks.

• Design, develop, and maintain end-to-end web applications using modern frontend and backend technologies. • Build responsive, high-performance user interfaces using React JS and related JavaScript or TypeScript frameworks. • Develop scalable backend services, RESTful APIs, and microservices using Node.js, Django, FastAPI, Flask, or similar frameworks. • Own full product development lifecycle activities including requirements analysis, architecture design, implementation, testing, deployment, monitoring, and continuous improvement. • Integrate AI capabilities into enterprise applications using LLMs, RAG pipelines, chatbot frameworks, and prompt engineering techniques. • Design and implement AI-enabled workflows including document ingestion, embeddings, vector search, retrieval optimization, and response generation. • Collaborate with product managers, UX designers, AI/ML engineers, DevOps teams, and business stakeholders to deliver reliable and user-focused solutions. • Ensure application security, scalability, performance, maintainability, and reliability across frontend, backend, database, and AI components. • Deploy, manage, and optimize applications and AI services on cloud platforms such as AWS, Google Cloud Platform (GCP), and Microsoft Azure. • Write clean, modular, well-tested, and maintainable code following software engineering best practices. • Mentor junior engineers, participate in code reviews, and contribute to technical design discussions and architecture decisions.