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C_SUITE

Chief Technology Officer

Yo Hr ConsultancyΒ·Posted 6 days ago

Location

Noida

Experience

5–8 years

Required Skills

PythonAWSGoogle Cloud PlatformCTOIT HeadNode.jsArtificial IntelligenceMachine Learning

About the Role

Role : Chief Technology Officer (CTO)


Location : Noida (6 Days work from Office)


Required Experience : Minimum of 5-8 Years of experience required.


Required Skills : Python, AI/ML, AWS / GCP, FastAPI, Langchain, RAG, CICD Pipeline, Software Architecture, Api Integrations, Nodejs, Reactjs, Leadership Skills


Must-Have :


- Minimum 5 to 7 years of experience in software engineering with at least 2+ years in a leadership capacity.


- Strong proficiency in Python (FastAPI, Flask, or Django).


- Experience integrating OpenAI/Gemini/Mistral/Whisper/LangChain.


- Solid experience with AI/ML model integration, LLMs, and RAG pipelines.


- Proven expertise in cloud deployment (AWS / GCP), Docker, and CI/CD.


- Strong understanding of backend architecture, API integrations, and system design.


- Experience building scalable, production-grade SaaS or conversational AI systems.


- Excellent communication and leadership skills - capable of interfacing with both engineers and clients.


Role Description :


We're looking for a hands-on Chief Technology Officer (CTO) to lead all technology initiatives for US business. This role is ideal for someone who combines strong engineering leadership with deep AI product-building experience someone who can code, lead, and innovate at the same time.


The CTO will manage the engineering team, guide AI development, interface with clients for technical requirements, and ensure scalable, reliable delivery of all platforms.


Role and Responsibilities :


A. Technical Leadership :


- Own end-to-end architecture, development, and deployment of AI-driven Voice & Text platforms.


- Work closely with the Founder to define the technology roadmap, ensuring alignment with business priorities and client needs.


- Oversee AI/ML feature development: including LLM integrations, automation workflows, and backend systems.


- Ensure system scalability, data security, uptime, and performance across all active deployments (US Projects).


- Collaborate with AI/ML engineers to guide RAG pipelines, conversational AI workflow design, and LLM prompt optimization.


B. Hands-On Contribution :


- Actively contribute to the core codebase (preferably Python/FastAPI/Node).


- Lead by example in code reviews, architecture design, and debugging.


- Experiment with LLM frameworks (OpenAI, Gemini, Mistral, etc.) and explore their applications in healthcare automation.


C. Product & Delivery Management :


- Translate client requirements into clear technical specifications and deliverables.


- Oversee product versioning, release management, QA, and DevOps pipelines.


- Collaborate with client success and operations teams to handle technical escalations, performance issues, and integration requests.


- Drive AI feature innovation - identify opportunities for automation, personalization, and predictive insights.


D. Team Management :


- Manage and mentor an 8-10 member engineering team.


- Conduct weekly sprint reviews, define coding standards, and ensure timely, high-quality delivery.


- Hire and train new engineers to expand client's technical capability.


- Foster a culture of accountability, speed, and innovation.


E. Client-Facing & Operational Ownership :


- Join client calls (US-based hospitals) to understand technical requirements or resolve issues directly.


- Collaborate with the founder on technical presentations and proof-of-concept discussions


- Handle A-Z of tech operations for the US business - infrastructure, integrations, uptime, and client satisfaction.


HireIQ AI InsightsBeta

Ideal Candidate

Someone who has scaled from IC to engineering lead (2+ years management) while maintaining hands-on depth in Python/FastAPI and cloud deployment.

Estimated Salary Range(medium confidence)

β‚Ή30 L – β‚Ή55 L per year

Likely Interview Questions

  1. 1.Walk us through a production LLM system you architected end-to-endβ€”what was your hands-on contribution versus team delegation, and how did you handle latency/cost optimization?
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