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3d ago

Automation & AI Engineer Associate

SAIC Washington, DC

Washington, DC

3d ago

$60k - $100k/yr Full time Backend Developer Agentic Ai Ai Agent

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Job Description

SAIC is modernizing legacy workloads into secure, cloud-native applications that embed AI and automation for a regulated federal financial environment. This onsite role in Washington, DC focuses on building production LLM and agentic solutions, integrating IRS-related data sources, and applying MLOps/AIOps and responsible AI controls to support governance and security requirements.

Salary: USD 60,001 - 100,000 per year.

What you will do

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

Required qualifications

Bachelor’s degree in Computer Science, Engineering, Data Science, or related field (or 4 years of 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.
Familiarity 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 such as LangChain and LangGraph.
Proficiency in Python and common ML/NLP libraries (e.g., Hugging Face, Transformers, scikit-learn, PyTorch, TensorFlow).
Production experience with AWS services including networking/IAM, Lambda, ECS/EKS, API Gateway, S3, DynamoDB, RDS, OpenSearch, SageMaker, and CloudWatch.
Practical experience using AWS Bedrock for LLM-powered applications and agents, including knowledge bases and guardrails.
Experience implementing RAG and working with vector search and vector 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 such as logging, metrics, and drift/quality monitoring for LLM and agent workflows.
Strong SQL skills and experience integrating legacy data into modern platforms.
Experience with Docker and container orchestration (Kubernetes, AWS ECS/EKS).

Technologies you may work with

Python, Hugging Face, Transformers, scikit-learn, PyTorch, TensorFlow
AWS (Lambda, ECS, EKS, API Gateway, S3, DynamoDB, RDS, OpenSearch, SageMaker, CloudWatch) and AWS Bedrock
LangChain, LangGraph, RAG, vector search
Terraform, CloudFormation, MLflow, Docker, Kubernetes
REST APIs, microservices, event-driven systems, CI/CD, MLOps, AIOps
LangSmith, CrewAI, AutoGen, Temporal, Model Context Protocol (MCP), BrainTrust, DeepEval, Databricks, Delta Lake

Desired experience

Strong technical judgment and communication skills 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 (for example, LangSmith or similar).
Experience with advanced agent frameworks such as CrewAI and AutoGen, including 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 for long-running AI and automation workflows.
Familiarity with Model Context Protocol (MCP) for tool integration and extensible agent systems.
Experience with LLM/agent evaluation frameworks such as BrainTrust or DeepEval, or similar.

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1mo ago

Senior AI Engineer

ServiceTitan, Inc. San Francisco, CA

San Francisco, CA

1mo ago Closed

$168k - $270k/yr Full time Backend Developer Python Ai Ml

This position is no longer accepting applications (closed Sep 28, 2026).

Job Description

ServiceTitan, Inc. is seeking a Senior AI Engineer to build scalable, low-latency machine learning systems and production services. The role focuses on taking models from development to deployment, including LLMs and other specialized models, with an emphasis on integrating them into real-time customer-facing experiences.

This position is based in California (onsite) and offers a USD 168,200 - 269,900 annual salary range. A minimum of 5 years of relevant experience is required.

What you’ll do

Design and implement scalable, low-latency machine learning systems and services, including model training, inference, and deployment infrastructure.
Partner with product teams to integrate, operationalize, and deploy machine learning models into production, including foundational models (LLMs), traditional ML models, and specialized models for speech, NLP, and forecasting.
Improve the performance and real-time responsiveness of ML pipelines to support a strong customer experience and minimize latency.
Communicate effectively with engineers, product managers, customers, and partners to align on outcomes and delivery.

What you bring

5+ years of experience writing production-level code in Python.
2+ years of experience deploying machine learning models (for example: NLP, speech, forecasting, or recommendation systems) to production at scale.
Experience designing, building, and deploying scalable ML inference services for real-time or high-throughput applications.
Advanced knowledge of machine learning methods and algorithms across traditional ML and deep learning theory and techniques.
Experience with reinforcement learning (RL) and implementing continuous learning or online optimization systems for production.
Understanding of the ML project lifecycle from experimentation to production deployment.
Experience with databases/data warehouses including SQL Server, PostgreSQL, Redshift, and Snowflake.
Experience with public cloud environments such as Azure or AWS.
Experience with microservices and asynchronous messaging technologies like Kafka and Azure Service Bus (critical for high-volume, real-time systems).
Experience with serverless architecture such as Azure Functions or AWS Lambda.
Experience with Azure Cognitive Services or similar cloud-based AI offerings is a plus.
Familiarity with Git, unit testing, debugging, profiling, JIRA, and other developer tools.
Comfort working in a fast-paced environment with a globally distributed team.

Tools and technologies

Python, Azure Cloud, AIOps, MLOps
LLMs, NLP, speech, forecasting
SQL Server, PostgreSQL, Redshift, Snowflake
Azure, AWS, Kafka, Azure Service Bus, Azure Functions, AWS Lambda
Azure Cognitive Services
Git, JIRA, microservices, serverless architecture

Benefits

Flextime, recognition, and support for autonomous work
Flexible time off with ample learning and development opportunities
Comprehensive onboarding program
Leadership training for Titans at all levels
Bonusly, peer-nominated awards, and more
Company-paid medical, dental, and vision (with 100% employer paid options and 90% coverage for dependents)
FSA and HSA
401k match
Telehealth options including memberships to One Medical
Parental leave and support
Up to $20k in fertility services (IUI and IVF)
Surrogacy and adoption reimbursement
On demand maternity support through Maven Maternity
Free breast milk shipping through Maven Milk
Pet insurance
Legal advisory services
Financial planning tools

Recruitment AI use

ServiceTitan uses technology, including automated and AI-assisted tools, to support certain aspects of the recruitment process. AI tools are not used to make hiring decisions; all hiring decisions are made by the hiring teams.

Be human with us

The team emphasizes that being human is not about checking every box. If you have the relevant skills but hesitate to apply due to background, the company encourages applying.

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