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 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.
(ref : hirist.tech)
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Ai Engineer • Pune
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