Senior AI Engineer
New
T
Talkspace CorporateMental Health / Behavioral Health
Remote, Eastern Standard TimeFull-TimeSenior
Salary169,000 - 200,000 USD per year
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Job Details
- Experience
- Minimum of 5+ years of experience in a production software engineering environment, with at least 3+ years specifically focused on designing, implementing, and deploying complex machine learning or autonomous systems.
- Required Skills
- AWSDockerPythonGCPKubernetesMachine LearningLLMMLOps
Requirements
- Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Robotics, Electrical Engineering, or a highly quantitative field.
- Minimum 5+ years experience in a production software engineering environment.
- At least 3+ years experience designing, implementing, and deploying complex machine learning or autonomous systems.
- Mastery of Python and its scientific libraries.
- Demonstrated experience building and managing CI/CD pipelines for ML models (MLOps).
- Strong theoretical and practical application of Reinforcement Learning (RL) or planning algorithms.
- Hands-on experience fine-tuning, deploying, and managing Large Language Models (LLMs) in production.
- Knowledge of prompt engineering, retrieval-augmented generation (RAG), and cost optimization.
- Proven ability to write high-quality, maintainable, scalable, and well-tested production code.
- Proficiency with cloud infrastructure (AWS, GCP) and containerization technologies (Docker, Kubernetes).
Responsibilities
- Design and implement the technical architecture for Sentia’s core AI agents, including planning modules, memory/retrieval systems, and tool-use orchestration.
- Apply advanced Reinforcement Learning (RL) or control theory methods to develop agents for adaptive intervention strategies.
- Select, fine-tune, and deploy Large Language Models (LLMs) to power agent reasoning and empathetic communication.
- Develop models for human-agent interaction incorporating principles from cognitive science and behavioral economics.
- Construct simulation environments for pre-training and testing agent policies to ensure system safety.
- Optimize deployment environments for low-latency decision-making and high system resilience.
- Design comprehensive evaluation pipelines including trace-level analysis, offline evaluation, real-time production monitoring, and guardrails.
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