AI / ML Engineer
About this role
A candidate who enjoys translating complex business needs into tangible technical solutions while building and deploying advanced AI systems would find this role suitable. This position is within Accenture Federal Services, a technology company dedicated to supporting the US federal government in enhancing national security and public safety. The team focuses on leveraging AI and Machine Learning to drive positive, lasting change across various government sectors. They will be responsible for the entire lifecycle of AI/ML models, from initial use case identification and design through to deployment and ongoing monitoring.
The individual will work extensively on designing, building, and fine-tuning Machine Learning and Generative AI models, including LLMs, RAG, and embeddings, utilizing platforms like Vertex AI and Gemini. Key activities involve developing robust, end-to-end ML pipelines, ensuring data quality, and implementing necessary governance and compliance standards for responsible AI. This role exists to ensure that the organization can effectively apply advanced AI capabilities to meet critical government missions.
This role is ideal for an experienced engineer who can bridge the gap between business strategy and complex ML engineering implementation. Success in this role requires strong technical depth in cloud AI services, solid pipeline development skills, and the ability to collaborate effectively across diverse technical and product teams.
What you will do
- Partner with stakeholders to identify and refine appropriate AI/ML use cases.
- Translate identified business needs into detailed technical solutions.
- Design, build, fine-tune, and evaluate ML and GenAI models, including LLMs, RAG, and embeddings, using Vertex AI and Gemini.
- Develop complete end-to-end ML pipelines covering data ingestion, feature engineering, orchestration, and CI/CD for models and prompts.
- Deploy scalable models and agents while managing monitoring, drift detection, and production troubleshooting.
- Collaborate with data engineering teams to establish high-quality data architecture using BigQuery, Dataflow, Pub/Sub, and Feature Store.
- Implement Responsible AI practices, security, governance, and compliance measures such as IAM and encryption.
- Perform hands-on experimentation, prototyping, EDA, hyperparameter tuning, and thorough documentation of pipelines.
What you need
- Possesses 3 to 6+ years of experience in machine learning engineering, data science, or AI development.
- Requires at least 3 years of experience leading technical teams to successfully achieve defined objectives and outcomes.
- Must have experience developing and implementing technical standards, systems, and processes for both cloud and on-prem environments.
- Should be capable of recommending technology strategies and decisions with a high level of expertise and knowledge.
- Must be a US Citizen and Public Trust Eligible.
- Demonstrates strong Python development skills and familiarity with core ML frameworks like TensorFlow, PyTorch, and scikit-learn.
- Requires a strong understanding of LLMs, embeddings, vector search, and generative AI techniques.
- Experience with Vertex AI Search, Agents, RAG solutions, or vector databases (e.g., Vertex Vector Search, Pinecone, Milvus) is beneficial.
Nice to have
- 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)
Benefits
- Collaborative and caring community
- Empowerment to grow, learn and thrive through hands-on experience
- Certifications and industry training
Skills and tech stack
Questions candidates ask
What is the remote policy for this role?
The posting does not specify a remote work policy for this position.
What is the salary range for this position?
The base pay range for this position in the listed locations is $103,200 — $196,400 USD.
What is the primary tech stack used for model development?
The core technology stack includes Python, ML frameworks such as TensorFlow and PyTorch, and cloud services like Vertex AI and Gemini APIs.
What kind of team collaboration is expected?
The role requires collaboration cross-functionally with product owners, platform teams, DevOps/SRE, and junior engineers to deliver reliable AI solutions.
The application is on the official Accenture Federal Services careers page.
Apply on company site