AI/ML Engineer

Accenture Federal Services

. Partner with stakeholders to identify and refine AI/ML use cases and translate business needs into technical solutions .

Posted 10/2/2026full-timeRemote • Virginia • United StatesMid-LevelSenior💰 $103,200 - $196,400 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in Machine Learning Engineering and AI Development, with a strong focus on designing and deploying scalable ML models and pipelines. Proficient in utilizing cloud-based platforms and tools, ensuring compliance with Responsible AI practices and technical standards.

Highest-signal resume keywords

Machine Learning EngineeringPython DevelopmentVertex AITensorFlowGoogle Cloud Professional Certification

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningAI DevelopmentData ScienceFeature EngineeringHyperparameter TuningModel DeploymentData IngestionGenerative AI TechniquesLLM Fine-TuningCloud-Based AI/ML Platforms

Soft Skills

CollaborationTechnical DirectionStakeholder EngagementTeam Leadership

Tools & Technologies

Vertex AIGemini APIsBigQueryDataflowPub/SubCloud StorageKubernetesGoogle Workspace EssentialsIAMKMS

Certifications & Qualifications

Google Cloud Professional Certification (ML Engineer)Google Cloud Professional Certification (Data Engineer)Google Cloud Professional Certification (Architect)

Industry Keywords

Responsible AIComplianceData ArchitectureCloud EnvironmentsRegulated Industry Experience

Tech Stack

Tools & technologies

AWSAzureBigQueryCloudFirebaseGoogle Cloud PlatformKubernetesMicroservicesPythonPyTorchScikit-LearnSQLTensorflow

About the role

Key responsibilities & impact

Partner with stakeholders to identify and refine AI/ML use cases and translate business needs into technical solutions
Design, build, fine-tune, and evaluate ML and GenAI models, including LLMs, RAG, embeddings, and deep learning, using Vertex AI, Gemini, and open-source tools
Develop end-to-end ML pipelines covering data ingestion, feature engineering, orchestration, and CI/CD for models and prompts
Deploy scalable models and agents; manage monitoring, drift detection, and production troubleshooting
Collaborate with data engineering teams on high-quality data architecture using BigQuery, Dataflow, Pub/Sub, and Feature Store
Implement Responsible AI, security, governance, and compliance best practices including IAM, encryption, and auditing
Work cross-functionally with product owners, platform teams, DevOps/SRE, and junior engineers to deliver reliable AI solutions
Perform experimentation, prototyping, exploratory data analysis, hyperparameter tuning, and documentation of pipelines and workflows

Requirements

What you’ll need

US Citizen (Public Trust Eligible)
3–6+ years in machine learning engineering, data science, or AI development
3+ years of experience leading technical teams to achieve objectives and outcomes
Experience developing and implementing technical standards, systems, and processes for cloud and on-prem environments
Experience recommending technology strategies and decisions with a high level of expertise and knowledge
Experience providing technical direction and support to ensure compliance with standards and guidelines
Google Storage experience, including access control, versioning, encryption, lifecycle management, logs, backups, static files, ML workflows, Storage Transfer Service, Cloud Storage, Cloud Storage for Firebase, Filestore, Google Workspace Essentials, Local SSD, and Persistent Disk
Python and SQL
TensorFlow, PyTorch, scikit-learn, Transformers, LLM fine-tuning, and RAG architectures
Vertex AI, Gemini APIs, BigQuery, Cloud Storage, KMS, and IAM
Vertex AI Pipelines, Dataflow, Pub/Sub, and Feature Store
Experience with Vertex AI Search, Agents, RAG solutions, or vector databases such as Vertex Vector Search, Pinecone, or Milvus
Experience deploying AI workloads on Kubernetes or microservices architectures
Google Cloud Professional certification (ML Engineer, Data Engineer, or Architect)
Hands-on experience with Vertex AI, Gemini APIs, or other cloud-based AI/ML platforms
Strong Python development skills and familiarity with ML frameworks
Strong understanding of LLMs, embeddings, vector search, and generative AI techniques
Must be authorized to work in the United States without current or future visa sponsorship
Preferred: knowledge of Responsible AI, bias mitigation, and model interpretability
Preferred: familiarity with GCP operational tools including IAM, KMS, Logging/Monitoring, VPC, and Cloud Storage
Preferred: exposure to AWS/Azure equivalents or third-party security, observability, and DevOps tools
Preferred: Master’s degree and prior federal or regulated-industry experience

Benefits

Comp & perks

Collaborative and caring community
Hands-on experience
Certifications
Industry training
Wide variety of benefits
Reasonable accommodations for disabilities or religious observances during recruiting and employment processes