Director, Distinguished Engineer, Enterprise AI Platforms
Mitsubishi UFJ Financial Group2 hours ago
Jersey City, NJ, USAStaff+
Base Salary
$250k - $350k/yr
Responsibilities
- Architect and evolve enterprise AI platform capabilities including LLM gateways, model routing, provider abstraction, agent runtimes, orchestration, prompt and context management, observability, and FinOps.
- Lead development of reusable model gateways, agent frameworks, tool registries, prompt libraries, evaluation pipelines, data connectors, orchestration patterns, and SDKs/APIs.
- Establish production-grade patterns for resilience, observability, latency, rate limiting, failover, caching, tenant fairness, usage attribution, cost optimization, automated testing, and production readiness.
- Embed governance, risk, security, privacy, monitoring, auditability, access controls, data classification, entitlement-aware retrieval, content filtering, and human oversight into platform architecture.
- Architect AI solutions using enterprise data, metadata, documents, ontologies, context graphs, knowledge layers, RAG, hybrid search, semantic retrieval, vector stores, and knowledge graphs.
- Create architecture roadmaps, reference architectures, design standards, and reusable patterns for agentic AI, multi-agent orchestration, knowledge graphs, evaluation at scale, developer tooling, and platform interoperability.
- Advise senior technology and business leaders on build-versus-buy decisions and evaluate emerging AI technologies, vendors, frameworks, observability products, vector databases, and AI security solutions.
- Mentor senior engineers, architects, and solution teams and communicate AI architecture concepts to executives, business sponsors, risk partners, and technical teams.
Requirements
- 15+ years of experience in enterprise software engineering, platform engineering, architecture, data platforms, distributed systems, or related technology leadership roles.
- Hands-on experience architecting and delivering production-grade AI, GenAI, LLM, data, or enterprise platform capabilities.
- Experience designing AI platform services such as LLM gateways, model routing, provider abstraction, RAG services, agentic workflows, AI observability, model evaluation, prompt and context management, and AI guardrails.
- Strong background in cloud-native architecture, APIs, microservices, event-driven systems, containerization, infrastructure automation, and production operations.
- Deep understanding of enterprise data architecture, data governance, metadata, lineage, structured and unstructured data integration, data quality, and access-control patterns.
- Experience with security, resilience, monitoring, auditability, cost optimization, and operational controls in regulated environments.
- Proven ability to influence architecture and engineering direction across multiple teams with strong executive communication skills.
- Familiarity with GenAI frameworks and tools such as LangChain, LangGraph, CrewAI, LiteLLM, Ragas, Hugging Face, PyTorch, TensorFlow, and SageMaker.
- Experience with model providers such as OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, Google Gemini, and open-weight models.
- Experience with data and knowledge platforms such as Snowflake, Databricks, Spark, Kafka, Airflow, OpenSearch, Elasticsearch, knowledge graphs, metadata platforms, and document intelligence.
- Strong engineering experience with Java, Python, Scala, JavaScript/TypeScript, SQL, Spring, FastAPI, Node.js, GraphQL, REST/OpenAPI, AWS, Kubernetes, Docker, Terraform, and GitHub.
- A bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or a related field is required.
Benefits
- Hybrid schedule requiring four days per week at an MUFG office or client site and one remote day.
- Comprehensive health and wellness benefits, retirement plans, educational assistance, and training programs.
- Income replacement for qualified employees with disabilities.
- Paid maternity and parental bonding leave, paid vacation, sick days, and holidays.
- The role may be eligible for discretionary performance-based bonus and/or incentive compensation.
Tech Stack
Apache AirflowApache KafkaApache SparkAWSDatabricksDockerElasticsearchFastAPIGraphQLJavaJavaScriptKubernetesNode.jsPythonPyTorchScalaSnowflakeSQLTensorFlowTerraformTypeScript