- Company Name
- Redolent, Inc
- Job Title
- Sr. AI/ML Engineer (Hybrid | W-2 Candidates Only)
- Job Description
-
Job Title: Sr. AI/ML Engineer
Role Summary: Design, develop, and deploy advanced AI/ML solutions within distributed, event‑driven systems. Lead technical architecture, API design, and observability for large‑scale LLM/AI Agent applications.
Expectations: Deliver end‑to‑end AI/ML features over a 6‑12 month contract. Provide technical leadership and maintain high code quality, performance, and reliability across cloud‑native environments.
Key Responsibilities
- Build and optimize LLM/AI Agent systems, including prompt engineering and deployment pipelines.
- Design and implement high‑throughput data pipelines, integrating upstream/downstream APIs.
- Develop RESTful and gRPC services with FastAPI and Python (async/await).
- Architect event‑driven workflows and distributed systems on Kubernetes.
- Implement observability using OpenTelemetry, Prometheus, and related tooling.
- Manage data stores: PostgreSQL, vector search, embeddings, and GCP BigQuery/GCS.
- Provide technical guidance and mentorship within cross‑functional teams.
- Ensure system scalability, reliability, and security throughout lifecycle.
Required Skills
- Expert Python (DSPy, FastAPI, async/await).
- Deep experience with LLMs, AI Agent systems, and prompt engineering.
- Proficient in distributed, event‑driven architecture design.
- Strong API design (REST, gRPC) and integration across services.
- Proficiency with observability tools (OpenTelemetry, Prometheus).
- Experience with PostgreSQL, vector search, embeddings.
- Hands‑on GCP (BigQuery, GCS), Kubernetes, and cloud‑native deployments.
- Knowledge of data pipeline development, system design, and API integration.
- Excellent technical leadership and communication skills.
Required Education & Certifications
- Bachelor’s degree in Computer Science, Engineering, or related field (Master’s preferred).
- Minimum 5 years of professional experience in AI/ML and distributed systems.
- Relevant certifications (e.g., GCP Professional Machine Learning Engineer, Terraform, Kubernetes) are advantageous.