Lead and manage the software engineering team, including task planning, technical guidance, mentoring, and performance monitoring of junior developers and engineers.
Drive AI-assisted software development practices using modern AI coding tools (e.g., Claude Code, vibe coding approaches) to improve development speed, quality, and efficiency while reducing dependency on traditional coding methods.
Oversee the development, integration, deployment, and maintenance of AI-driven applications, enterprise platforms, APIs, automation workflows, and related software systems.
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Act as a primary technical counterpart and architecture partner - designing, coding, and deploying a suite of custom, intelligent AI tools, and critically reviewing design decisions.
Enhance our system's ability to manage context windows, optimize token usage, and reduce attention noise when connecting LLMs to large codebase repositories.
Design and extend graph based code and document representations (e.g. property graphs, AST derived structures) and hybrid retrieval pipelines (dense + sparse + reranking/fusion) over vector databases.
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Model & Prompt Engineering: Design, test, and iterate prompts that improve reasoning, factuality, and user experience.
Agentic AI Frameworks: Build, manage and orchestrate agentic workflows involving tool calling, reasoning loops, memory management, skills orchestration, and multi-step task execution.
AI Safety & Reliability: Implement guardrails, monitoring, and validation mechanisms to ensure AI systems remain safe, reliable, and ethically grounded
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Lead and conduct in-depth technical due diligence on potential M&A targets, strategic partners, or new venture opportunities, assessing their R&D capabilities, technology stack, intellectual property (IP) portfolio, and product/process maturity.
Evaluate the technical feasibility, scalability, and integration challenges of external technologies or solutions. Identify technical risks and potential synergies between the company's existing R&D efforts and external opportunities.
Proactively monitor emerging technologies, scientific breakthroughs, and industry trends relevant to the company's strategic interests and R&D roadmap. Identify potential technology partners, startups, or research institutions for collaboration, investment, or acquisition.
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Design, develop, deploy, and maintain AI Agents to automate business workflows and operational processes.
Build internal AI assistants capable of handling business tasks such as document processing, knowledge retrieval, customer support, reporting, and workflow orchestration.
Integrate Large Language Models (LLMs) into business applications to enhance productivity and decision-making.
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deliver complex machine learning pipelines across data preparation, model development, evaluation, deployment, and monitoring
perform applied research on deep learning models for medical image segmentation, detection, and classification across imaging modalities (e.g. radiographs, CBCT, volumetric data)
design rigorous evaluation frameworks by preparing clinically meaningful metrics and test sets
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Contribute toward AI roadmap for AI-driven automation aligning with strategic company and client goals.
Collaborate with engineering and data teams to design and architect scalable, robust, and innovative AI solutions (e.g., automated network diagnostics, bot recommendation systems, AI agents NOC operations).
Act as the Solution Owner in an Agile/Scrum environment, managing the product backlog, writing detailed user stories, defining acceptance criteria, and prioritizing features.
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Design, build and train machine learning and deep learning models for classification, regression, clustering, time series forecasting, recommendation systems and more.