SUNIL THAKUR

• Drove enterprise-wide AI transformation, establishing strategic roadmaps and scalable architectures for cloud and hybrid environments.
• Engineered Agentic AI systems with LangGraph and LangChain to manage LLMs and RAG pipelines for financial and public sector clients.
• Architected data and vector infrastructure, achieving 35% faster response times in real-time reasoning workflows.
• Established AI Ops frameworks, including CI/CD automation and model monitoring, to enhance reliability and scalability.
• Advised C-level executives on AI governance and responsible deployment frameworks for model lifecycle maturity.
• Standardized AI engineering practices across regional teams, increasing production deployment speed by 25%.
• Mentored cross-functional squads to integrate GenAI fluency and agile experimentation into workflows.
• Championed algorithmic optimization initiatives, improving large-scale models for energy efficiency and latency reduction.

Architected deep reinforcement learning frameworks for optimizing industrial water recovery, enhancing efficiency by 20–25%.

• Developed adaptive LSTM-based time-series prediction models to forecast plant performance under varying conditions.
• Established continuous validation pipelines with automated retraining and model drift detection in hybrid cloud/on-prem environments.
• Standardized machine learning best practices, ensuring testing, reproducibility, and governance for industrial scalability.
• Researched and implemented innovative AI techniques for anomaly detection and predictive maintenance in smart infrastructure.
• Created domain-specific machine learning toolkits, reducing deployment time by 30% and accelerating experimentation cycles.
• Mentored data scientists and engineers, promoting a culture of applied research and experimentation across teams.
• Collaborated with R&D and product teams to transform prototypes into production-ready AI solutions.

• Technical leadership to develop and drive ML best practices and manage strategic ML projects.
• Research and implement appropriate ML algorithms and related tools for scalability.
• Test, validate and maintain ML models, data pipelines and related applications built on Cloud and on-prem solutions.
• Evaluate the efficacy of specific methods and techniques for given ML problems and edge cases.
• Create software design, implementation and testing as per company's engineering standards.
• Work with Time Series data sets and maintain methods for baselining and continuous model validation (CI/CD).
• Keep current of latest AI research relevant to business domains and mentor team members.

• Lead AI Project for business use-cases with Business Units.
• Help AI product managers and business stakeholders understand the potential and limitations of AI when planning new products.
• Build AI models to help product managers and stakeholders understand results.
• Set up AI development infrastructure and Build data ingest and data transformation infrastructure.
• Deploy AI models into production and Create APIs to put results of your AI models into operations.

• Transformed business requirements into AI-driven embedded sensor intelligence solutions.
• Established AI integration framework in embedded systems, combining signal processing with machine learning.
• Developed and optimized Linux drivers and Android middleware for sensor evaluation applications.
• Integrated machine learning algorithms into Android stacks to enhance sensing accuracy.
• Designed and maintained firmware and software components for full system functionality.
• Provided technical support to application and field engineers for efficient problem resolution.
• Delivered product launch support via technical marketing and live demonstrations at major events.
• Collaborated with mobile OEMs on seamless implementation of new system software technologies.

• Engineered boot code and low-level networking software for Cisco’s PowerPC-based CPT systems, increasing fault tolerance and startup reliability.
• Developed embedded firmware and device drivers for Linux and Android platforms, integrating with advanced networking chipsets.
• Created embedded applications for telecom and IoT systems, achieving seamless hardware-software integration.
• Led software development for WLAN, WiMAX, and LTE systems, delivering next-generation connectivity solutions.
• Collaborated with global cross-functional teams to provide comprehensive wireless platform solutions.