MLOps Engineer building production ML infrastructure, pipelines, and monitoring for exacare ai’s post - acute care AI platform. Scaling reliable systems that help healthcare teams make safer placement decisions.
About the role
- Senior AI/ML Engineer leading design and deployment of high-impact AI solutions at CloudWerx. Bridging predictive modeling with next-generation Agentic AI to solve client challenges.
Responsibilities
- 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.
Requirements
- 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.
Benefits
- Competitive Compensation – Market-aligned salary reflecting your expertise and impact
- Remote Work Flexibility – Work in a remote first organization, but can still collaborate at multiple offices globally.
- Comprehensive Health Benefits – Medical, dental, vision, and wellness coverage for you and your family.
- Flexible Paid Time Off – Take the time you need with a results-focused approach
- Professional Development – Work with industry leaders, and learn from the most talented engineers in the industry.
- Google Cloud Training & Certifications – Access to leading cloud education resources.
- High-Impact Client Work – Enterprise engagements shaping the future of Cloud, Data Platforms, and Agents
- Collaborative Culture – Professional, transparent, and team-oriented environment.
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