AI - ML Engineer

Key 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.

Here is what you need:

• 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.

Preferred Experience:

• 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)