Automation and AI Engineer

Saicinc — United States · Posted ~3 hours ago

Mid Full-time Remote

Skills

AI engineering Automation Software development Cloud-native applications AI Cloud

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Summary ✨ AI‑Generated

An engineering role focused on applying AI and automation to transform legacy applications into secure, modern cloud solutions.

Highlights

Opportunity to modernize complex systems using AI and cloud technologies while solving challenging engineering problems.

Description

Job ID 2617370 Location Washington, DC, US Date Posted 2026-09-29 Category Software Subcategory SW Engineer Schedule Full-Time Shift Day Job Travel No Minimum Clearance Required None Clearance Level Must Be Able to Obtain Public Trust Potential for Remote Work ORA_REMOTE Description 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.Ability to obtain and maintain a public trust requiring U.S. Citizenship or Green Card. 9+ years in software, ML, or data engineering, including experience with application modernization.4+ years building and deploying 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). Target salary range $120,001 - $160,000. The estimate displayed represents the typical salary range for this position based on experience and other factors.