Artificial Intelligence Engineer

The AI / Agentic AI Engineer works across: (1) large-scale data pipeline development processing market events in a cloud environment, and (2) primarily, design and development of agentic AI systems including LLM-powered regulatory data assistants, MCP servers, and agent harness architectures. This position contributes to overall product quality throughout the software development lifecycle. Build and maintain ETL/ELT pipelines using Apache Spark, Hive, and Trino across S3-based data lake environments Develop and optimize SQL for large-scale surveillance datasets including window functions, multi-table joins, and complex aggregations Build and engineer big data systems (EMR-on-EC2, EMR-on-EKS) and develop solutions on analytical platforms (SageMaker, Domino, Dataiku) Participate in data quality monitoring, anomaly detection, and production incident investigation Develop AI agent systems using AWS Bedrock and agent frameworks (Strands Agents SDK, LangChain/LangGraph, or equivalent) Build agent harness architectures combining LLM reasoning with deterministic execution - skill/RAG-based SQL generation and structured output validation Implement agent memory, context management, and tool integration (MCP servers, API connectors, data catalog lookups) across the data lake Build evaluation frameworks for agent accuracy - paraphrase robustness, routing precision, and structural consistency Stay informed of advances in LLM frameworks (LangGraph, Google ADK, AWS Strands) and emerging AI capabilities Write clean, well-tested code; contribute to CI/CD Jenkins pipelines and infrastructure-as-code on AWS Ensure secure handling of RCI and sensitive regulatory data across both data pipelines and agent outputs - auditable execution traces Adhere to CLIENT and team standards for secure development practices and technology policies Partner across teams, communicate technical information at the appropriate level, and maintain documentation on Confluence/Wiki Actively learn from senior team members; contribute to process improvement in line with CLIENT's values of collaboration, expertise, innovation, and responsibility Essential Technical Skills Experience building data pipelines using Apache Spark (PySpark preferred) and SQL Experience with SQL query engines (Hive, Trino/Presto, or similar) and cloud data platforms (AWS S3, EMR, Lambda) Understanding of common issues like data skew and strategies to mitigate it, working with large data volumes, and troubleshooting job failures due to resource limitations, bad data, and scalability challenge Real-world experience with debugging and mitigation strategies Generative AI & Agentic Systems Practical experience building LLM-powered agent systems that use tools and produce structured outputs (not just chatbot interfaces) Hands-on experience with at least one agent framework: LangChain, LangGraph, AWS Strands, or equivalent Working knowledge of prompt engineering, RAG architectures, and context/memory management Experience with foundation model APIs (Anthropic Claude, Amazon Nova, OpenAI, or similar) Memory Architecture: Understanding of agent memory tiers - working memory, episodic memory, semantic memory - and strategies for context persistence, pruning, and retrieval across session Agent Harness Design: Familiarity with harness patterns that wrap LLM reasoning with deterministic guardrails, tool routing, verification loops, and graceful degradation Hands-on experience with AI development tools (GitHub Copilot, Q Developer, ChatGPT, Claude, etc.) Experience with spec-driven development - using structured specifications to guide AI code generation, review, and validation Ability to leverage AI pair programming for code suggestions, debugging, refactoring, and automated test generation Experience with AWS services like S3, EMR, EMR on EKS, Lambda, Bedrock, Step Functions, etc Hands-on experience using S3 with Spark (e.g., dealing with file formats, consistency issues Familiarity with AWS Bedrock for foundation model invocation, knowledge bases, guardrails, and agent orchestration Exposure to Google Cloud Vertex AI (model garden, grounding, agent builder) or equivalent managed AI platforms Familiarity with AWS monitoring and logging tools (CloudWatch, CloudTrail) for production workloads Programming – Python Proficiency in Python for data engineering and automation Ability to write clean, modular, and performant code Experience with functional programming concepts (e.g., immutability, higher-order functions) Strong understanding of collections, concurrency, and memory management SQL Skills (Window Functions, Joins, Complex Queries) Proficiency with SQL window functions, multi-table joins, and aggregations Ability to write and optimize complex SQL queries Experience handling edge cases like NULLs, duplicates, and ordering Good to Have AWS Bedrock AgentCore (memory, identity, tool gateway) Model Context Protocol (MCP) server development and integration Agent evaluation harnesses and agentic patterns (draft-verification, compile-style generation) Fine-tuning foundation models for domain-specific tasks (LoRA, PEFT, or managed fine-tuning via Bedrock/Vertex AI) Local model execution with Ollama, vLLM, or similar for development and experimentation Vector databases (FAISS, Pinecone, OpenSearch) Docker, Kubernetes, and Amazon EKS for containerized workloads Infrastructure as Code (Terraform, CloudFormation) Experience with CI/CD pipelines (Jenkins, GitLab CI, GitHub Actions, ArgoCD) Experience with monitoring and observability tools (Prometheus, Grafana, ELK stack AWS certifications (AI Practitioner, Solutions Architect, or Kubernetes certifications like CKA/CKAD) Education / Experience Requirement Bachelor's degree in Computer Science, Data Science, Information Systems, or related discipline with at least two (2) years of related experience; or equivalent training and/or work experience; past Financial Services industry experience preferred Demonstrated technical expertise in Object Oriented and database technologies/concepts which resulted in deployment of enterprise quality solutions Extensive knowledge of industry leading software engineering approaches including Test Automation, Build Automation and Configuration Management frameworks #J-18808-Ljbffr Unisys