AI Engineer
17 hours ago
Job Description :
Key Responsibilities :
- Design, develop, and deploy AI/ML models and pipelines to support product features and internal automation needs.
- Build and integrate agentic workflows that allow AI agents to operate across multiple systems, take actions, and orchestrate end-to-end processes within our SaaS platform.
- Leverage AWS Bedrock and other cloud-native AI platforms to deploy and scale LLMs in production environments.
- Work closely with product managers, engineers, and business analysts to translate customer workflows into AI-powered solutions.
- Fine-tune and optimize LLMs and other generative AI models for domain-specific use cases in behavioral healthcare.
- Develop AI agents and services that improve efficiency in documentation, claims, credentialing, and patient management workflows.
- Collaborate with data engineering teams to ensure high-quality, structured datasets for training and evaluation.
- Evaluate, benchmark, and integrate third-party AI APIs and tools where beneficial.
- Monitor model performance in production, ensuring reliability, scalability, and compliance with US healthcare requirements.
- Implement guardrails, prompt engineering techniques, and responsible AI practices to deliver safe, accurate, and explainable outputs.
- Partner with QA and DevOps to ensure robust testing, deployment, and monitoring of AI features
- Stay updated on the latest advancements in AI/ML and proactively suggest applications relevant to healthcare SaaS.
Requirements :
- 4+ years of experience in AI/ML engineering, with at least 2+ years hands-on experience deploying models in production environments.
- Strong programming skills in Python and familiarity with frameworks like PyTorch, LangChain or similar.
Requirements.
- Experience building AI agentic workflows (multi-step reasoning, tool use, action execution) in real-world applications.
- Hands-on experience with AWS Bedrock, SageMaker, or other cloud-native AI/ML services.
- Experience with fine-tuning and deploying large language models (LLMs) or generative AI solutions.
- Solid understanding of NLP, information retrieval, embeddings, and prompt engineering.
- Experience working in enterprise SaaS environments; familiarity with healthcare or insurance domains is a strong plus.
- Understanding of data engineering concepts (ETL pipelines, SQL/NoSQL databases, APIs, cloud data storage).
- Ability to balance research and experimentation with pragmatic product-driven implementation.
- Excellent problem-solving skills and strong collaboration across cross-functional, globally distributed teams.
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