AI/ML Engineer

This is a fully remote position, open to applicants in Virginia.

📋 Description

• Collaborate with stakeholders to pinpoint and enhance AI/ML use cases, transforming business requirements into technical solutions.

• Design, construct, optimize, and assess ML and GenAI models, such as LLMs, RAG, embeddings, and deep learning, utilizing Vertex AI, Gemini, and open-source tools.

• Create comprehensive ML pipelines that encompass data ingestion, feature engineering, orchestration, and CI/CD for models and prompts.

• Deploy scalable models and agents, overseeing monitoring, drift detection, and troubleshooting in production.

• Work alongside data engineering teams to establish a high-quality data architecture using BigQuery, Dataflow, Pub/Sub, and Feature Store.

• Enforce Responsible AI practices, along with security, governance, and compliance best practices, such as IAM, encryption, and auditing.

• Collaborate cross-functionally with product owners, platform teams, DevOps/SRE, and junior engineers to deliver dependable AI solutions.

• Conduct experimentation, prototyping, exploratory data analysis, hyperparameter tuning, and documentation of pipelines and workflows.

⛳️ Requirements

• Must be a US Citizen (Public Trust Eligible).

• 3–6+ years of experience in machine learning engineering, data science, or AI development.

• At least 3 years of experience leading technical teams to achieve goals and deliver outcomes.

• Proven experience in developing and implementing technical standards, systems, and processes in both cloud and on-premises environments.

• Demonstrated ability to recommend technology strategies and decisions based on a high level of expertise and knowledge.

• Experience in providing technical guidance to ensure compliance with established standards and guidelines.

• Familiarity with Google Storage, including access control, versioning, encryption, lifecycle management, logs, backups, static files, ML workflows, Storage Transfer Service, Cloud Storage, Cloud Storage for Firebase, Filestore, Google Workspace Essentials, Local SSD, and Persistent Disk.

• Proficiency in Python and SQL.

• Knowledge of TensorFlow, PyTorch, scikit-learn, Transformers, LLM fine-tuning, and RAG architectures.

• Experience with Vertex AI, Gemini APIs, BigQuery, Cloud Storage, KMS, and IAM.

• Familiarity with Vertex AI Pipelines, Dataflow, Pub/Sub, and Feature Store.

• Experience with Vertex AI Search, Agents, RAG solutions, or vector databases like Vertex Vector Search, Pinecone, or Milvus.

• Background in 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 knowledge of ML frameworks.

• In-depth understanding of LLMs, embeddings, vector search, and generative AI techniques.

• Must be eligible to work in the United States without current or future visa sponsorship.

• Preferred: Familiarity with Responsible AI, bias mitigation, and model interpretability.

• Preferred: Knowledge of GCP operational tools including IAM, KMS, Logging/Monitoring, VPC, and Cloud Storage.

• Preferred: Experience with AWS/Azure equivalents or third-party security, observability, and DevOps tools.

• Preferred: Master’s degree and prior experience in federal or regulated industries.

🏝️ Benefits

• A collaborative and supportive community.

• Hands-on experience opportunities.

• Access to certifications.

• Industry-specific training.

• A diverse range of benefits.

• Reasonable accommodations for disabilities or religious observances during the recruitment and employment processes.