Accenture Federal Services
Accenture Federal Services
Accenture Federal Services teikia technologijų ir konsultavimo paslaugas Jungtinių Valstijų federalinei vyriausybei. Būdama „Accenture“ dalimi, įmonė dirba konsultavimo, gynybos ir logistikos srityse, padėdama federalinėms agentūroms spręsti sudėtingus veiklos ir misijos poreikius pasitelkiant debesijos sprendimus, kibernetinį saugumą, duomenis ir dirbtinį intelektą, skaitmeninę inžineriją bei kitas besivystančias technologijas. Įmonės veikla apima saugių ir lengvai plečiamų skaitmeninių sistemų kūrimą, duomenimis grindžiamų įžvalgų taikymą sprendimams gerinti ir valstybės infrastruktūros modernizavimą. Įmonė taip pat remiasi moksliniais tyrimais bei plėtra ir į žmogų orientuotu projektavimu, kad stiprintų atsparų ir veiksmingą viešojo sektoriaus darbą.

AI/ML Engineer – Remote in Virginia

Design and deploy Vertex AI and generative AI solutions for U.S. federal agencies. Build production machine learning pipelines, intelligent agents, and secure, governed cloud architectures.

Aprašymas

  • Work with stakeholders to define AI and machine learning use cases and convert business requirements into technical solutions
  • Design, develop, fine-tune, and assess machine learning and generative AI models using Vertex AI, Gemini, LLMs, RAG, embeddings, deep learning, and open-source tools
  • Build complete machine learning pipelines for data ingestion, feature engineering, orchestration, and model and prompt CI/CD
  • Deploy scalable models and agents while overseeing monitoring, drift detection, and production issue resolution
  • Partner with data engineering teams to develop reliable data architectures using BigQuery, Dataflow, Pub/Sub, and Feature Store
  • Apply Responsible AI, security, governance, and compliance practices, including IAM, encryption, and auditing
  • Coordinate with product owners, platform teams, DevOps/SRE professionals, and junior engineers to deliver dependable AI systems
  • Conduct experiments, prototypes, exploratory analysis, hyperparameter tuning, and documentation for pipelines and workflows

Reikalavimai

  • U.S. citizenship and eligibility for Public Trust clearance
  • 3–6 or more years of experience in machine learning engineering, data science, or AI development
  • At least 3 years of experience leading technical teams toward defined objectives and outcomes
  • Experience creating and implementing technical standards, systems, and processes across cloud and on-premises environments
  • Demonstrated expertise in recommending technology strategies and making technical decisions
  • Experience providing technical guidance and support to maintain compliance with standards and policies
  • Experience with Google Storage capabilities, including access control, versioning, encryption, lifecycle management, logging, backups, static files, machine learning workflows, Storage Transfer Service, Cloud Storage, Cloud Storage for Firebase, Filestore, Google Workspace Essentials, Local SSD, and Persistent Disk
  • Proficiency in Python and SQL
  • Experience with TensorFlow, PyTorch, scikit-learn, Transformers, LLM fine-tuning, and RAG architectures
  • Experience with Vertex AI, Gemini APIs, BigQuery, Cloud Storage, KMS, and IAM
  • Experience with Vertex AI Pipelines, Dataflow, Pub/Sub, and Feature Store
  • Experience with Vertex AI Search, AI agents, RAG solutions, or vector databases such as Vertex Vector Search, Pinecone, or Milvus
  • Experience deploying AI workloads through Kubernetes or microservices architectures
  • Google Cloud Professional certification in Machine Learning Engineer, Data Engineer, or Cloud Architect
  • Hands-on experience with Vertex AI, Gemini APIs, or comparable cloud AI and machine learning platforms
  • Advanced Python development skills and familiarity with machine learning frameworks
  • Strong knowledge of LLMs, embeddings, vector search, and generative AI methods
  • Authorization 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 services such as IAM, KMS, Logging/Monitoring, VPC, and Cloud Storage
  • Preferred: exposure to AWS or Azure equivalents and third-party security, observability, or DevOps tools
  • Preferred: master’s degree and experience in federal or regulated industries

Privalumai

  • Supportive, collaborative workplace community
  • Practical, hands-on project experience
  • Professional certification opportunities
  • Industry-focused training
  • Broad benefits offering
  • Reasonable accommodations for disabilities or religious observances during recruitment and employment

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