AI / ML Engineer

Accenture Federal Services•Arlington, VA

12d•$103,200 - $196,400

About The Position

At Accenture Federal Services, our purpose is to help the US federal government make the nation stronger and safer, and improve the lives of its citizens. Our team of over 13,000 professionals is dedicated to leveraging technology and innovation for clients across defense, national security, public safety, civilian, and military health organizations. As part of the global Accenture, Accenture Federal Services is a technology company recognized by Glassdoor as a Top 100 Best Place to Work. We foster a supportive and collaborative environment where employees feel a sense of belonging and are empowered to grow through hands-on experience, certifications, and industry training. Join us to drive meaningful and lasting change that advances missions and the government forward.

Requirements

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.

Nice To Haves

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)

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.