Applied ML Engineer working on AI - driven insights at Kaseya. Collaborating with product teams to enhance features with machine learning and data analysis.
About the role
- Lead AI/ML & MLOps Engineer executing projects from data foundations to model deployment. Collaborating with sales to drive AI/ML engagements for our clients.
Responsibilities
- Delivery and technical leadership
- Lead the architecture and hands-on implementation of end-to-end ML systems: data ingestion, pipelines, feature stores, training, evaluation, serving, and monitoring
- Own technical decisions across the full stack, data platform, training environment, model serving, and MLOps tooling
- Set engineering standards for ML projects: experiment tracking, model versioning, reproducibility, governance, observability, drift monitoring, and CI/CD for ML
- Coach and uplift other engineers on the team in modern ML and MLOps practices
- Stay accountable for quality, security, and operational soundness of what we ship
- Pre-Sales and pipeline support
- Partner with the sales leadership team across pre-sales activity: discovery calls, scoping workshops, technical briefings, and LOE preparation
- Lead architecture and solutioning conversations with prospects and customers, translate business problems into credible, defensible technical approaches
- Provide dedicated technical support to opportunities flowing through the partners sales process, including positioning their products as part of broader data and AI architectures, joint solutioning sessions, and partner-aligned proposals
- Contribute to thought leadership and demand generation: blog posts, webinars, capability decks, conference talks, and reference architectures
Requirements
- Machine Learning fundamentals
- Strong grounding in the full ML lifecycle: data pipeline creation, feature engineering, model training, evaluation, deployment, and monitoring
- Production experience designing and building data pipelines that feed ML workloads (batch and streaming)
- Solid hands-on understanding of model training: hyperparameter tuning, validation strategies, dealing with class imbalance, leakage, common failure modes
- Ability to select appropriate model families (classical ML, deep learning, large language models) for the problem at hand and justify the choice
- Hands-on production experience with the core MLOps building blocks: Model registry and model versioning Experiment tracking and reproducibility Training pipelines and orchestration CI/CD for ML (model and data) Model serving (online, batch, streaming) Model observability, performance, drift, data quality, and operational metrics Governance, lineage, and access control
- Experience with at least one major MLOps / experiment platform, for example MLflow, Weights & Biases, Vertex AI, SageMaker, Azure ML, or Databricks, is required. Cross-platform experience is preferred
- Production experience building and operating ML systems on at least one major cloud: GCP, AWS, or Azure
- Strong comfort with the data and AI services on that cloud (e.g. BigQuery / Vertex AI, Redshift / SageMaker, Synapse / Azure ML)
- Cross-cloud experience and the ability to make pragmatic platform recommendations is a strong plus
- Practical experience with model explainability techniques: SHAP, LIME, feature attribution, partial dependence, model cards
- Familiarity with responsible AI practices: bias evaluation, fairness, calibration, uncertainty quantification, and confidence-aware UX patterns (e.g. withholding low-confidence predictions)
- Awareness of what it takes to make a model trustworthy in regulated or high-stakes domains
- Hands-on experience designing and shipping agentic AI solutions in production or production-adjacent settings
- Strong understanding of common agent design patterns, ReAct, plan-and-execute, tool use, reflection, multi-agent orchestration, human-in-the-loop
- Working experience with one or more agent frameworks (e.g. LangChain / LangGraph, LlamaIndex, CrewAI, etc.) and vector databases
- Strong working knowledge of modern data platforms, relational, NoSQL, warehouse, and lakehouse.
- MongoDB experience (Atlas, Atlas Vector Search, change streams, schema design for analytical and AI workloads) is highly valued
- Familiarity with BigQuery, Snowflake, and Databricks is a plus
- Comfortable in a consulting setting: multiple concurrent engagements, ambiguity, scoping under time pressure, and frequent client interaction
- Strong written and verbal communication, able to hold a technical conversation with a CTO and explain a model decision to a non-technical or business stakeholder in the same hour
- Prior experience supporting pre-sales activity (scoping, technical proposals) is strongly preferred
- Comfortable being on camera and in the room with prospects and partners.
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