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Role Summary As a Senior AI/ML Engineer, you will lead the design and deployment of high-impact AI solutions, expertly bridging traditional predictive modeling with next-generation Agentic AI. You are equally comfortable tuning a Gradient Boosted Tree for structured data as you are architecting a multi-agent system for complex reasoning tasks. You will collaborate directly with clients to understand their needs, translate business challenges into technical solutions, and provide expert guidance on AI/ML best practices. The role requires overseeing dataset quality for model training and leveraging GCP for efficient model deployment and scaling. You will utilize the Google AI ecosystem (Vertex AI, Google ADK) and orchestration frameworks like LangChain/LangGraph to move AI solutions from experimental demos to reliable, production-grade systems grounded in statistical rigor. Role and Responsibility Architect and implement sophisticated multi-agent systems and autonomous workflows leveraging the Google AI SDK, LangGraph, and LangChain to solve complex, non-linear business processes. Lead the design and construction of cloud-native solutions, using Terraform, Kubernetes, and Docker to ensure that AI models are deployed on scalable, reliable infrastructure. Apply rigorous statistical evaluation frameworks to model performance, moving beyond standard metrics to include uncertainty estimation, calibration, and robust hypothesis testing during model optimization (LoRA, QLoRA). Lead the development of custom predictive models and deep learning solutions, utilizing frameworks like PyTorch and Scikit-Learn to select suitable architectures—whether decision trees, neural nets, or ensembles—based on performance and client criteria. Design and implement state-of-the-art generative models for NLP and multimodal tasks, leveraging tools like OpenCV for image preprocessing and Stable Diffusion concepts where applicable. Champion MLOps best practices within the team, building validated data pipelines and CI/CD/CT workflows using Kubeflow and Vertex AI Pipelines to ensure model quality and integrity. Collaborate directly with clients to understand their unique needs, translating business challenges into technical solutions and providing expert guidance on dataset management best practices. Personally tackle the most difficult engineering challenges, identifying technical risks such as overfitting or latency issues, and optimizing hyperparameters to ensure precision and interpretability. Required Experience and Qualifications 7+ years of technical experience, with at least 3+ years focused on ML/AI and 1 year in a consulting capacity. Experience building and evaluating agentic loops, including tool-use (function calling), self-reflection, and multi-step reasoning architectures. Deep proficiency in the modern Python AI stack, including extensive experience with core libraries (NumPy, Pandas, PyTorch) and specialized LLMOps/Agentic tools for monitoring and evaluation (e.g., LangSmith, Braintrust, AgentOps, or HoneyHive) Proven track record of building AI/ML solutions for users, including experience with GenAI common solutions like Vertex AI, OpenAI API, and vector database technologies. Strong foundation in probabilistic modeling, Bayesian statistics, and experimental design (A/B testing for AI) to ensure model reliability and groundedness. Excellent verbal and written communication skills, with the ability to confidently articulate complex AI concepts to business, technical, and non-technical stakeholders. Education/Certifications Google Cloud: Professional Machine Learning Engineer Industry: Databricks Certified Machine Learning Professional OR DeepLearning.AI Generative AI Specialization. Education in: Computer Science, Mathematics, Machine Learning/Data Science What We Offer Competitive Compensation – Market-aligned salary reflecting your expertise and impact Remote W