Spectrum San Diego

Automation & AI Engineer Associate

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2 Locations

In-Office or Remote

Entry level

Security

The Role

Build and deploy AI- and LLM-powered automation solutions while modernizing legacy federal financial systems into secure, cloud-native architectures. Responsibilities include developing agentic AI applications, RAG and vector-search solutions, integrating legacy data, implementing MLOps and CI/CD practices, and applying security, privacy, governance, and observability controls. The role requires collaboration with architects, developers, testers, and stakeholders, with emphasis on AWS, Bedrock, Python, modern application architecture, and production AI operations.

Summary Generated by Built In

SAIC is seeking a self-motivated, customer-focused Automation & AI Engineer to join our team. You will work with AI Engineers, Developers, and Testers to modernize legacy systems into intelligent, secure, cloud-native applications.

You will help design and enhance a highly secure system for a federal agency that processes high-value financial transactions over the Internet. Experience with payment systems, trading systems, or other highly secure transactional environments is a strong plus. This role is ideal for someone who enjoys solving complex problems with modern AI and cloud technologies in a collaborative team environment.

Key Responsibilities

Modernize GMF and related legacy workloads by refactoring monoliths and batch processes into secure, cloud-native architectures (microservices, APIs, event-driven systems) with embedded AI/automation.
Design, build, and deploy LLM- and agentic AI–based solutions (e.g., LangChain, LangGraph, RAG, vector search, AWS Bedrock agents) that automate complex workflows and integrate with IRS data sources.
Implement platform engineering and MLOps/AIOps best practices, including CI/CD, infrastructure-as-code, model/prompt lifecycle management, and responsible AI controls.
Collaborate with architects, developers, testers, and stakeholders to design scalable, secure AI-driven modernization solutions.
Integrate legacy data sources into modern data platforms and AI-enabled services.
Ensure compliance with security, privacy, and governance requirements in a regulated federal financial environment.

Qualifications

Required

Bachelor’s degree in Computer Science, Engineering, Data Science, or related field.(4 years experience in lieu of degree)
Ability to obtain and maintain a public trust requiring U.S. Citizenship or Green Card.
0+ years in software, ML, or data engineering, including experience with application modernization.
Familiar with AI/ML or LLM-based applications in production.
Strong experience with modern application architectures (microservices, REST APIs, event-driven) and legacy integration.
Hands-on experience building agentic AI solutions using LLM frameworks (e.g., LangChain, LangGraph).
Proficiency in Python and common ML/NLP libraries (e.g., Hugging Face, Transformers, scikit-learn, PyTorch/TensorFlow).
Production experience with AWS (networking/IAM, Lambda, ECS/EKS, API Gateway, S3, DynamoDB, RDS, OpenSearch, SageMaker, CloudWatch).
Practical experience using AWS Bedrock for LLM-powered applications and agents (knowledge bases, guardrails).
Experience implementing RAG and working with vector search/databases.
Experience with CI/CD and infrastructure-as-code (e.g., Terraform, CloudFormation).
Familiarity with MLOps/AIOps (e.g., MLflow, SageMaker) and AI-focused observability (logging, metrics, drift/quality monitoring) for LLM/agent workflows.
Strong SQL skills and experience integrating legacy data into modern platforms.
Experience with Docker and container orchestration (Kubernetes, AWS ECS/EKS).

Desired

Strong technical judgment and communication skills; able to explain AI modernization approaches to technical and business stakeholders.
Experience with Databricks (notebooks, Delta Lake, ML/feature store) for data and ML pipelines.
Experience with LLM/agent observability and debugging tools (e.g., LangSmith or similar).
Experience with advanced agent frameworks (e.g., CrewAI, AutoGen) and multi-agent workflows.
Hands-on experience operating agents in production (safety/guardrails, performance tuning, lifecycle management).
Experience with durable workflow engines (e.g., Temporal) for long-running AI/automation workflows.
Familiarity with Model Context Protocol (MCP) for tool integration and extensible agent systems.
Experience with LLM/agent evaluation frameworks (e.g., BrainTrust, DeepEval, or similar).

About UsSAIC® is a premier mission integrator focused on advancing the power of technology and innovation to serve and protect our world. Our robust portfolio of offerings across the defense, space, intelligence, and civilian markets includes secure high-end solutions in mission IT, enterprise IT, engineering services, and professional services. We integrate emerging technology, rapidly and securely, into mission critical operations that modernize and enable critical national imperatives.

We are approximately 23,000 strong; driven by mission, united by purpose, and inspired by opportunities. SAIC is an Equal Opportunity Employer. Headquartered in Reston, Virginia, SAIC has annual revenues of approximately $7.3 billion. For more information, visit saic.com. For ongoing news, please visit our newsroom.

Skills Required

Bachelor's degree in Computer Science, Engineering, Data Science, or a related field; four years of experience may substitute for the degree.
Ability to obtain and maintain a Public Trust requiring U.S. Citizenship or Green Card.
0+ years of software, machine learning, or data engineering experience, including application modernization.
Familiarity with AI/ML or LLM-based applications in production.
Strong experience with microservices, REST APIs, event-driven architectures, and legacy integration.
Hands-on experience building agentic AI solutions with LLM frameworks such as LangChain or LangGraph.
Proficiency in Python and common ML/NLP libraries such as Hugging Face, Transformers, scikit-learn, PyTorch, or TensorFlow.
Production experience with AWS, including networking, IAM, Lambda, ECS/EKS, API Gateway, S3, DynamoDB, RDS, OpenSearch, SageMaker, and CloudWatch.
Practical AWS Bedrock experience for LLM applications and agents, including knowledge bases and guardrails.
Experience implementing RAG and working with vector search or databases.
Experience with CI/CD and infrastructure as code, such as Terraform or CloudFormation.
Familiarity with MLOps/AIOps and AI-focused observability, including logging, metrics, drift, and quality monitoring.
Strong SQL skills and experience integrating legacy data into modern platforms.
Experience with Docker and container orchestration, including Kubernetes or AWS ECS/EKS.
Strong technical judgment and communication skills.
Experience with Databricks, notebooks, Delta Lake, and ML or feature stores.
Experience with LLM or agent observability and debugging tools such as LangSmith.
Experience with advanced agent frameworks such as CrewAI or AutoGen and multi-agent workflows.
Hands-on experience operating agents in production, including safety, guardrails, performance tuning, and lifecycle management.
Experience with durable workflow engines such as Temporal.
Familiarity with Model Context Protocol for tool integration and extensible agent systems.
Experience with LLM or agent evaluation frameworks such as BrainTrust or DeepEval.