AI / ML Engineer
Arlington, VA
- Pay
USD 103,200–196,400/yearAnnual period assumed — pay source
The pay range for the states of California, Colorado, Connecticut, Hawaii, Illinois, Maine, Maryland, Massachusetts, Minnesota, New Jersey, New York, Ohio, Vermont, Virginia, Washington, and the District of Columbia is: $103,200—$196,400 USD What We Believe
Read the full posting- Work setup
- Unconfirmed
- Employment
- Unconfirmed
Before you apply
- Sponsorship
Visa sponsorship not confirmed — sponsorship source
Applicants for employment in the US must have work authorization that does not now or in the future require sponsorship of a visa for employment authorization in the United States.
Read the full posting
What you’ll work on
Full postingPartner with stakeholders to identify and refine AI/ML use cases; translate business needs into technical solutions.
Design, build, fine‑tune, and evaluate ML and GenAI models (LLMs, RAG, embeddings, deep learning) using Vertex AI, Gemini, and open‑source tools.
Develop end‑to‑end ML pipelines, including data ingestion, feature engineering, orchestration, and CI/CD for models and prompts.
From the employer’s posting
Key Responsibilities: Partner with stakeholders to identify and refine AI/ML use cases; translate business needs into technical solutions. Design, build, fine‑tune, and evaluate ML and GenAI models (LLMs, RAG, embeddings, deep learning) using Vertex AI, Gemini, and open‑source tools.
Partner with stakeholders to identify and refine AI/ML use cases; translate business needs into technical solutions. Design, build, fine‑tune, and evaluate ML and GenAI models (LLMs, RAG, embeddings, deep learning) using Vertex AI, Gemini, and open‑source tools. Develop end‑to‑end ML pipelines, including data ingestion, feature engineering, orchestration, and CI/CD for models and prompts.
Design, build, fine‑tune, and evaluate ML and GenAI models (LLMs, RAG, embeddings, deep learning) using Vertex AI, Gemini, and open‑source tools. Develop end‑to‑end ML pipelines, including data ingestion, feature engineering, orchestration, and CI/CD for models and prompts. Deploy scalable models and agents; manage monitoring, drift detection, and production troubleshooting.
What you’ll bring
All qualificationsPreferred experience
- Master’s degree; prior federal or regulated industry experience (FedRAMP, HIPAA, NIST)
- Knowledge of Responsible AI, bias mitigation, and model interpretability
- Familiarity with GCP operational tools (IAM, KMS, Logging/Monitoring, VPC, Cloud Storage)
Qualification wording
Master’s degree; prior federal or regulated industry experience (FedRAMP, HIPAA, NIST)
Knowledge of Responsible AI, bias mitigation, and model interpretability
Familiarity with GCP operational tools (IAM, KMS, Logging/Monitoring, VPC, Cloud Storage)
Tools in this posting
- Python
- BigQuery
- Google Cloud (GCP)
- Kubernetes
- PyTorch
- TensorFlow
- SQL
- AWS
- Azure
- scikit-learn
Source — Tool mentions in context
- Google Storage: Access control, versioning, encryption, lifecycle management, storing logs, handling backups, managing static files, working with ML workflows, Storage Transfer Service, Cloud Storage, Cloud Storage for Firebase, Filestore, Google Workspace Essentials, Local SSD, Persistent Disk - Languages: Python, SQL - ML & GenAI: TensorFlow, PyTorch, scikit-learn, Transformers, LLM fine tuning, RAG architectures
- Hands-on experience with Vertex AI, Gemini APIs, or other cloud-based AI/ML platforms. - Strong Python development skills and familiarity with ML frameworks (TensorFlow, PyTorch, scikit-learn). - Strong understanding of LLMs, embeddings, vector search, and generative AI techniques.
- Deploy scalable models and agents; manage monitoring, drift detection, and production troubleshooting. - Collaborate with data engineering teams to ensure high‑quality data architecture using BigQuery, Dataflow, Pub/Sub, and Feature Store. - Implement Responsible AI, security, governance, and compliance best practices (IAM, encryption, auditing).
- ML & GenAI: TensorFlow, PyTorch, scikit-learn, Transformers, LLM fine tuning, RAG architectures - Cloud: Vertex AI, Gemini APIs, BigQuery, Cloud Storage, KMS, IAM - Data Pipelines: Vertex AI Pipelines, Dataflow, Pub/Sub, Feature Store
- 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.
- Knowledge of Responsible AI, bias mitigation, and model interpretability - Familiarity with GCP operational tools (IAM, KMS, Logging/Monitoring, VPC, Cloud Storage) - Exposure to AWS/Azure equivalents or third-party tools (security, observability, DevOps)
- Experience with Vertex AI Search, Agents, RAG solutions, or vector databases (e.g., Vertex Vector Search, Pinecone, Milvus). - Experience deploying AI workloads on Kubernetes or microservices architectures - Google Cloud Professional certification (ML Engineer, Data Engineer, or Architect)
- Languages: Python, SQL - ML & GenAI: TensorFlow, PyTorch, scikit-learn, Transformers, LLM fine tuning, RAG architectures - Cloud: Vertex AI, Gemini APIs, BigQuery, Cloud Storage, KMS, IAM
- Familiarity with GCP operational tools (IAM, KMS, Logging/Monitoring, VPC, Cloud Storage) - Exposure to AWS/Azure equivalents or third-party tools (security, observability, DevOps) As required by local law, Accenture Federal Services provides reasonable ranges of compensation for hired roles based on labor costs in the states of California, Colorado, Connecticut, Hawaii, Illinois, Maine, Maryland, Massachusetts, Minnesota, New Jersey, New York, Ohio, Vermont, Virginia, Washington, and the District of Columbia. The base pay range for this position in these locations is shown below. Compensation for roles at Accenture Federal Services varies depending on a wide array of factors, including but not limited to office location, role, skill set, and level of experience. Accenture Federal Services offers a wide variety of benefits. You can find more information on benefits here. We accept applications on an on-going basis and there is no fixed deadline to apply.
Job description
The work:
Key Responsibilities:
- Partner with stakeholders to identify and refine AI/ML use cases; translate business needs into technical solutions.
- Design, build, fine‑tune, and evaluate ML and GenAI models (LLMs, RAG, embeddings, deep learning) using Vertex AI, Gemini, and open‑source tools.
- Develop end‑to‑end ML pipelines, including 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 to ensure high‑quality data architecture using BigQuery, Dataflow, Pub/Sub, and Feature Store.
- Implement Responsible AI, security, governance, and compliance best practices (IAM, encryption, auditing).
- Work cross‑functionally with product owners, platform teams, DevOps/SRE, and junior engineers to deliver reliable AI solutions.
- Perform hands‑on experimentation, prototyping, EDA, hyperparameter tuning, and documentation of pipelines and workflows.
Here is what you need:
- US Citizen (Public Trust Eligible)
- 3–6+ years in machine learning engineering, data science, or AI development.
- 3+ years of experience in leading technical teams to achieve objectives and outcomes. Experience includes
- Developing and implementing technical standards, systems and processes for cloud and on-prem environments.
- Recommending technology strategies and decisions with a high-level of expertise and knowledge.
- Providing technical direction and support to ensure compliance with standards and guidelines
- Google Storage: Access control, versioning, encryption, lifecycle management, storing logs, handling backups, managing static files, working with ML workflows, Storage Transfer Service, Cloud Storage, Cloud Storage for Firebase, Filestore, Google Workspace Essentials, Local SSD, Persistent Disk
- Languages: Python, SQL
- ML & GenAI: TensorFlow, PyTorch, scikit-learn, Transformers, LLM fine tuning, RAG architectures
- Cloud: Vertex AI, Gemini APIs, BigQuery, Cloud Storage, KMS, IAM
- Data Pipelines: Vertex AI Pipelines, Dataflow, Pub/Sub, Feature Store
- Experience with Vertex AI Search, Agents, RAG solutions, or vector databases (e.g., Vertex Vector Search, Pinecone, 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 (TensorFlow, PyTorch, scikit-learn).
- Strong understanding of LLMs, embeddings, vector search, and generative AI techniques.
Preferred Experience:
- Master’s degree; prior federal or regulated industry experience (FedRAMP, HIPAA, NIST)
- Knowledge of Responsible AI, bias mitigation, and model interpretability
- Familiarity with GCP operational tools (IAM, KMS, Logging/Monitoring, VPC, Cloud Storage)
- Exposure to AWS/Azure equivalents or third-party tools (security, observability, DevOps)
As required by local law, Accenture Federal Services provides reasonable ranges of compensation for hired roles based on labor costs in the states of California, Colorado, Connecticut, Hawaii, Illinois, Maine, Maryland, Massachusetts, Minnesota, New Jersey, New York, Ohio, Vermont, Virginia, Washington, and the District of Columbia. The base pay range for this position in these locations is shown below. Compensation for roles at Accenture Federal Services varies depending on a wide array of factors, including but not limited to office location, role, skill set, and level of experience. Accenture Federal Services offers a wide variety of benefits. You can find more information on benefits here. We accept applications on an on-going basis and there is no fixed deadline to apply.
Your next step
- Have your CV and examples of relevant work ready.
- Check the listed location, eligibility and core experience before starting.
Complete your application on boards.greenhouse.io. The employer’s form will show what is required.
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Source & posting history
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
The pay range for the states of California, Colorado, Connecticut, Hawaii, Illinois, Maine, Maryland, Massachusetts, Minnesota, New Jersey, New York, Ohio, Vermont, Virginia, Washington, and the District of Columbia is: $103,200—$196,400 USD What We Believe
- Location & working pattern
Arlington, VA
Working pattern and location restrictions need checking in the full posting.
- Work authorization
Here is what you need: - US Citizen (Public Trust Eligible) - 3–6+ years in machine learning engineering, data science, or AI development.
More source context
Equal Employment Opportunity Statement We believe that no one should be discriminated against because of their differences. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other basis as protected by federal, state, or local law. Our rich diversity makes us more innovative, more competitive, and more creative, which helps us better serve our clients and our communities. For details, view a copy of the Accenture Federal Services Equal Opportunity Policy Statement. Accenture Federal Services is an Equal Employment Opportunity employer. Additionally, as an Affirmative Action Employer for Veterans and Individuals with Disabilities, Accenture Federal Services is committed to providing veteran employment opportunities to our service men and women.
More relevant text appears in the full description.
- Status in our records
- Active
- First seen by us
- Oct 7, 2026
- Recorded sightings
- 5
- Employer says posted
- Oct 2, 2026
These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.
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