AI/ML Engineer

Job Title:

( AI/ML Engineer )

About Kyyba:

Founded in 1998 and headquartered in Farmington Hills, MI, Kyyba has a global presence delivering high-quality resources and top-notch recruiting services, enabling businesses to effectively respond to organizational changes and technological advances.

At Kyyba, the overall well-being of our employees and their families is important to us. We are proud of our work culture which embodies our core values; incorporating value, passion, excellence, empowerment, and happiness, creates a vibrant and productive atmosphere.

We empower our employees with the resources, incentives, and flexibility that they need to support a healthy, balanced, and fulfilling career by providing many valuable benefits and a balanced compensation structure combined with career development.

Job Description

Position Description: Employees in this job function are responsible for designing, building, deploying and scaling complex self-running ML solutions in areas like computer vision, perception, localization etc. They also automate and optimize the end-to-end ML model lifecycle using their expertise in experimental methodologies, statistics, and coding for tool building and analysis.

Key Responsibilities

1) Collaborate with business and technology stakeholders to understand current and future ML requirements 2) Design and develop innovative ML models and software algorithms to solve complex business problems in both structured and unstructured environments 3) Design, build, maintain and optimize scalable ML pipelines, architecture and infrastructure 4) Use machine language and statistical modeling techniques such as decision trees, logistic regression, Bayesian analysis and others to develop and evaluate algorithms to improve product/system performance, quality, data management and accuracy 5) Adapt machine learning to areas such as virtual reality, augmented reality, object detection, tracking, classification, terrain mapping, and others.

6) Train and re-train ML models and systems as required 7) Deploy ML models and algorithms into production and run simulations for algorithm development and test various scenarios 8) Automate model deployment, training and re-training, leveraging principles of agile methodology, CI/CD/CT (Continuous Integration/ Continuous Deployment/ Continuous Training) and MLOps 9) Enable model management for model versioning and traceability to ensure modularity and symmetry across environments and models for ML systems

Skills Required: GCP, Big Query, Python, Java, Cloud Infrastructure, Artificial Intelligence & Expert Systems

Skills Preferred:

Graph Layer • Design, develop, test, and deploy the ISDP knowledge graph from the domain event store to Production using cloud-native data pipelines. • Model and evolve the graph's entities and relationships as new data sources are onboarded.

MCP serving layer • Design, build, and operate the MCP server that exposes the ISDP graph layer and event store as tools to consumers — including GQL graph query, event-store query, and schema/DDL discovery. • Define tool contracts, context, and guardrails so agents produce grounded, accurate, non-hallucinated responses over the graph. • Ensure low-latency, secure, and cost-efficient serving for interactive and batch agent workloads.

Reliability, monitoring & observability • Own monitoring and observability of the graph layer and the MCP server — data freshness, pipeline health, query latency/cost, tool-call success rates, and answer quality. • Instrument SLOs, dashboards, alerting, and tracing; drive incident response and continuous reliability improvements.

Collaboration & data onboarding • Partner with Data Engineers and Application Data Source Owners across Product Development, Manufacturing, Quality, and Supply Chain to ingest and validate their data into ISDP. • Establish data contracts, schema validation, and quality checks; support source owners through onboarding, mapping to the ISDP logical model, and troubleshooting. • Contribute to data governance, cataloging, and lineage for the graph and its sources.

Experience required

Years in AI and Graph Engineering • Strong software engineering in Java, Python, with production-grade testing, CI/CD, and code quality practices. • Hands-on experience deploying data/AI systems to Production on a GCP-native stack: Vertex AI, BigQuery, Dataflow / Apache Beam, Pub/Sub, Cloud Run / GKE, Cloud Storage, and Cloud Build / Artifact Registry. • Experience with graph data modeling and querying — property graphs and GQL / graph query patterns (BigQuery property graphs, or equivalent such as Neo4j/Spanner Graph). • Hands-on experience with Vertex AI (Agents, model serving, embeddings) and evaluation of agent answer quality. • Experience building LLM/agent systems: tool-use, RAG/grounding, and integrating models via APIs (e.g., Vertex AI or enterprise LLM gateways).

Familiarity with MCP or comparable agent tool protocols. • Observability expertise: Cloud Monitoring/Logging, OpenTelemetry, SLOs, dashboards, and alerting for data pipelines and services. • Infrastructure as Code (Terraform) and secure-by-default engineering (IAM, least privilege, secrets management). • Ability to work directly with data producers to model and validate real-world industrial/enterprise data.nanij@kyyba.com