Job Description :

Primary Responsibilities :

- Design and build AI-powered features using NLP/LLMs and classical ML for provider chat and voice workflows

- Deliver end to end: data preparation, modeling/experimentation, evaluation, API integration, deployment, and monitoring in cloud environments

- Implement responsible AI guardrails (content safety, PHI/PII controls, prompt hygiene, evaluation suites) and collaborate with governance/review partners

- Develop and maintain web services and APIs that expose ML capabilities; write clear documentation and runbooks

- Apply MLOps practices (CI for ML, model registry, reproducible experiments) and contribute to on call/operational support with mentorship

- Collaborate with product, architecture, security, data, and platform teams to align with enterprise standards and deliver measurable outcomes

- Continuously improve code quality, test coverage, latency, reliability, and cost per inference

Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

Required Qualifications :

- 3+ years of total software engineering experience, including 2+ years hands on in AI/ML (e.g., NLP, GenAI, predictive modeling, or recommendations)

- Experience building and integrating model backed APIs/services (Python/Node/Java) into production systems

- Working knowledge of a major cloud (preferably AWS): containers/serverless, IAM basics, storage/networking fundamentals, logging/metrics

- Understanding of security, access control, monitoring/alerting, and foundational resiliency/DR concepts for cloud applications

- Solid software engineering practices: version control, code reviews, automated testing, and CI/CD

- Solid Python skills with one or more ML/DL frameworks (PyTorch/TensorFlow; familiarity with Hugging Face or LangChain is a plus)

Preferred Qualifications :

- Bachelors degree in Computer Science, Engineering, Mathematics, or related field (or equivalent practical experience)

- Exposure to healthcare data/processes (e.g., claims, appeals, prior authorization, provider operations) or other regulated industries

- Experience with conversational AI (intent/entity models, retrieval augmented generation, grounding) and vector databases

- Familiarity with MLOps toolchains (feature stores, pipelines, model registry) and evaluation frameworks (toxicity, hallucination, bias, drift)

- Full stack curiosity (UI integrations, design systems) and a habit of sharing learnings via demos, office hours, or internal wikis