AI / ML Engineer

Description

The company is a technology business within the global Accenture group, working for the US federal government.

Its 13,000+ people serve clients in defense, national security, public safety, civilian and military health.

The work aims to move federal missions forward and bring lasting change.

Responsibilities

• Work with stakeholders to pin down AI and ML use cases and turn business needs into technical solutions.
• Build, fine-tune and evaluate ML and GenAI models, including LLMs, RAG, embeddings and deep learning, on Vertex AI, Gemini and open-source tools.
• Deploy scalable models and agents, then monitor them, catch drift and troubleshoot production issues.
• Create end-to-end ML pipelines covering data ingestion, feature engineering, orchestration and CI/CD for models and prompts.
• Work with data engineers on data architecture using BigQuery, Dataflow, Pub/Sub and Feature Store.
• Apply Responsible AI, security, governance and compliance practices such as IAM, encryption and auditing.
• Partner across product, platform, DevOps/SRE and junior engineering teams to deliver dependable AI solutions.
• Run hands-on experiments and prototypes, including EDA and hyperparameter tuning, and document pipelines and workflows.

Requirements

• Be a US citizen who is eligible for Public Trust.
• Bring 3 to 6+ years in ML engineering, data science or AI development.
• Have 3+ years of leading technical teams toward objectives and outcomes.
• Have experience setting technical standards, systems and processes for cloud and on-prem environments.
• Have experience recommending technology strategies and decisions at a high level of expertise.
• Have experience giving technical direction so work meets standards and guidelines.
• Have hands-on Google Cloud Storage skills: access control, versioning, encryption, lifecycle management, logs, backups, static files and ML workflows.
• Have hands-on use of Storage Transfer Service, Cloud Storage for Firebase, Filestore, Google Workspace Essentials, Local SSD and Persistent Disk.
• Write Python and SQL.
• Work with TensorFlow, PyTorch, scikit-learn, Transformers, LLM fine-tuning and RAG architectures.
• Use Vertex AI, Gemini APIs, BigQuery, Cloud Storage, KMS and IAM.
• Build data pipelines with Vertex AI Pipelines, Dataflow, Pub/Sub and Feature Store.
• Hold a Google Cloud Professional certification as ML Engineer, Data Engineer or Architect.
• Have hands-on experience with Vertex AI, Gemini APIs or other cloud-based AI and ML platforms.
• Have strong Python development skills and know ML frameworks.
• Understand LLMs, embeddings, vector search and generative AI techniques well.
• Have experience with Vertex AI Search, agents, RAG solutions or vector databases such as Vertex Vector Search, Pinecone or Milvus.
• Have experience deploying AI workloads on Kubernetes or microservices architectures.
• Preferred: a master's degree, and federal or regulated industry experience with FedRAMP, HIPAA or NIST.
• Preferred: knowledge of Responsible AI, bias mitigation and model interpretability.
• Preferred: familiarity with GCP operations tools such as IAM, KMS, Logging and Monitoring, VPC and Cloud Storage.
• Preferred: exposure to AWS or Azure equivalents, or third-party security, observability and DevOps tools.

Conditions

Pay ranges are shared as local law requires for 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 in those locations is $103,200 - $196,400 USD.

Pay varies with office location, role, skill set and experience level.

Applications are accepted on an ongoing basis with no fixed deadline.

The company is committed to a culture where everyone is respected, belongs and has equal opportunity.

Benefits

• A broad set of benefits is offered, and details are available from the company.
• Opportunities to grow through hands-on experience, certifications and industry training.