Back
Tier-1 VC-backed startup, metro location, popular AI engineer title, and broad generalist responsibilities increase competition.
Healthcare domain work and regulator-aware guardrails increase domain specificity, though core ML skills remain transferable.
Requires applied ML production experience, LLM and backend expertise, and safety guardrails.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Own end-to-end build and delivery of AI applications for healthcare clients including providers and payors.
Translate client problems into production AI systems focused on document understanding, conversational agents, retrieval, and workflow automation, integrating Sarvam's language and speech models.
Ensure systems are reliable and safe in the field, ship rapidly across multiple projects, and improve accuracy under real-world load.
Several years of hands-on engineering experience building and shipping real AI/ML applications (beyond prototypes).
Proficiency with AI stacks including LLMs, prompting, retrieval-augmented generation (RAG), fine-tuning, evaluation, and agentic systems.
Strong backend engineering skills, ideally in Python, with experience owning production services.
Work Experience Required: Several years in applied AI engineering; Notice period: Not explicitly mentioned in the JD.
Experienced in working directly with healthcare clients (providers, payors, diagnostics, pharma) or familiar with healthcare data and workflows.
Comfortable iterating quickly in ambiguous, high-agility environments and owning problems end-to-end.
Skilled at shipping reliable AI systems in high-stakes domains, especially involving Indian languages and regulated data privacy considerations.