AI Solution Architect

Job Description -

Qualifications:

• Bachelor’s or master’s degree in computer science, Engineering, or a related field.

• Experience in architecture roles with a focus on manufacturing and industrial applications.

• 7 years in distributed systems or platform engineering, with 3 years in AI/Knowledge/ML infrastructure.

• Strong expertise in microservices, event-driven systems, and API platform design.

• Hands-on experience with one or more of:

• Vector DBs / Graph Stores: Weaviate, Neo4j, ArangoDB, RedisGraph, Milvus.

• Knowledge/Metadata Services: OpenMetadata, DataHub, Amundsen, or custom catalog services.

• Orchestration & Pipelines: Airflow, Dagster, Ray, LangGraph, or similar.

• Skilled in Python, Go, or TypeScript, with strong fundamentals in API development, caching, and messaging systems (Kafka, SQS, etc.).

• Familiarity with retrieval architectures (RAG, hybrid retrieval) and semantic search design principles.

• Experience embedding governance and policy hooks (RBAC, lineage, audit) in data or AI pipelines.

• Proficient in traditional and modern architectural patterns, software development, and system integration.

• Demonstrated ability to think strategically and integrate a broad range of ideas regarding product development.

• Excellent communication and interpersonal skills, with the ability to work effectively with cross-functional teams and manage stakeholder expectations.

• Proven leadership abilities and experience in working with diverse and globally distributed teams.

Preferred Qualifications:

• Experience with Generative, DL/ML/AI, IoT, and data analytics in a manufacturing environment.

• Certifications in cloud architecture (AWS Certified Solutions Architect, Google Cloud Professional Architect, etc.).

• Prior experience in agile development environments.

• Familiarity with multi-agent orchestration frameworks (LangGraph, ServiceNow Agentic AI, Azure Foundry, Bedrock AgentCore).

• Prior experience designing developer platforms or internal PaaS for AI or knowledge systems.

• Exposure to policy enforcement and observability (OpenTelemetry, SPIFFE/SPIRE, OPA).

• Experience with schema/ontology evolution tooling and event-driven synchronization patterns