AI / ML Engineer
About The Position
At Accenture Federal Services, our purpose is to help the US federal government make the nation stronger and safer, and improve people's lives. Our 13,000+ employees are dedicated to leveraging technology and ingenuity for clients across defense, national security, public safety, civilian, and military health organizations. Join Accenture Federal Services, a technology company within the global Accenture network. We are recognized as a Glassdoor Top 100 Best Place to Work, offering a collaborative and supportive community where you can thrive through hands-on experience, certifications, and industry training. Drive positive, 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.
Benefits
- Accenture Federal Services offers a wide variety of benefits.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
