New opportunity

AI/ML/Gen AI Engineer

Ascendion · Bengaluru, India

About this role

The AI/ML/Gen AI Engineer role at Ascendion is designed to support a talent pipeline ranging from recent college graduates to experienced Senior/Lead engineers. The position focuses on building and maintaining internal AI platforms within a regulated healthcare environment. Responsibilities include: Core Responsibilities: Design, build, test, and operate reusable AI-enabled core capabilities, including automation frameworks, shared agents, orchestration, and guardrails. Convert field-proven patterns into scalable, hardened, and well-documented platform capabilities. Utilize AI-assisted development tools and agentic frameworks to maintain the internal AI platform. Establish and maintain reliability, observability, security, and governance controls. Provide reusable APIs, libraries, and templates for infrastructure teams. Analyze, design, code, test, and implement technical solutions with minimal defects. Collaborate with Forward Deployed Engineers, architecture, security, and platform teams to align with enterprise standards. Document technical processes, participate in reviews, and stay current with emerging technologies. Experience Requirements: The role is leveled based on experience: Entry (Associate) level requires 0-2 years (recent graduates welcome), Intermediate level requires 2-5 years, and Senior/Lead level requires 5+ years. Scope and autonomy increase with progression, from contributing components with guidance to owning the architecture and direction of core platform capabilities.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Artificial IntelligenceAutomation FrameworksOrchestrationReliability EngineeringObservabilitySecurity ControlsGovernanceAPI DevelopmentAgentic FrameworksAI-assisted development

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Platform Reliability and Governance in Healthcare
The role requires building AI capabilities within a regulated healthcare environment. You must prioritize the implementation of robust security, observability, and governance controls to ensure that all AI-enabled tools meet strict compliance standards while maintaining high system reliability.
Development of Reusable AI Infrastructure
A core duty is to design and operate reusable AI-enabled capabilities, including orchestration, automation frameworks, and shared agents. You must focus on creating high-quality APIs, libraries, and templates that allow other infrastructure teams to integrate these AI tools seamlessly.
Hardening Field-Proven Patterns
You are expected to convert experimental patterns from Forward Deployed Engineers into scalable, hardened, and well-documented platform capabilities. This requires a focus on transitioning from prototype-level code to production-ready, maintainable software.

Likely challenges to think through

Balancing Innovation with Regulatory Constraints
Working in a regulated healthcare environment often limits the speed of deployment for new AI models. Prepare by demonstrating how you maintain rigorous documentation and security protocols without stifling the agility required to iterate on AI-assisted development tools.
Cross-Functional Integration Complexity
You must collaborate with architecture, security, and platform teams to align with enterprise standards. The challenge lies in reconciling the specific needs of AI agents with broader infrastructure requirements; prepare by highlighting your experience in technical negotiation and cross-team communication.
Maintaining Scalable Agentic Frameworks
As the platform grows, managing agentic frameworks and orchestration becomes increasingly complex. Prepare by discussing your approach to observability and monitoring, ensuring that automated agents remain performant and predictable as the system scales.

Practice your answers

Technical practice

Designing AI Agent Orchestration

What to coverExplain how you would design an orchestration layer for multiple AI agents to ensure they work cohesively. Discuss the trade-offs between centralized versus decentralized control and how you would handle inter-agent communication and task delegation.

Implementing Guardrails in Gen AI

What to coverIn a regulated healthcare environment, what strategies would you use to implement guardrails for LLM outputs? Address how you would balance model creativity with the need for strict factual accuracy and safety compliance.

Observability for AI Platforms

What to coverHow do you define and measure observability for an AI-driven platform beyond standard system metrics? Suggest specific metrics for monitoring model performance, drift, and latency in production environments.

Scalable API Design for AI Capabilities

What to coverWhen building reusable APIs for infrastructure teams, what design principles do you follow to ensure ease of integration? Discuss how you handle versioning and documentation for AI-enabled services.

Security in Regulated AI Environments

What to coverWhat specific security controls are necessary when deploying AI models in a healthcare context? Focus on data privacy, secure model access, and preventing prompt injection or data leakage.

AI-Assisted Development Workflows

What to coverDescribe how you integrate AI-assisted development tools into your coding process while maintaining code quality. How do you validate the code generated by AI to ensure it meets enterprise standards?

Hardening Field-Proven Patterns

What to coverDescribe your process for taking a prototype or a field-proven pattern and converting it into a hardened, production-ready platform capability. What documentation and testing steps are critical during this transition?

Automation Frameworks for AI

What to coverHow do you approach building automation frameworks specifically for testing AI-enabled systems? Discuss the challenges of testing non-deterministic outputs and how you ensure consistent results.

Behavioural practice

Collaborating Across Teams

What to coverDescribe a situation where you had to collaborate with cross-functional teams like security or architecture to align a technical solution. How did you handle conflicting priorities or feedback?

Adapting to Technical Ambiguity

What to coverGive an example of a time you had to work on a project with evolving requirements or emerging technology. How did you ensure your work remained aligned with the overall project goals?

Ownership and Autonomy

What to coverTell me about a time you took ownership of a technical component or architecture. How did you ensure the quality of the delivery and manage the expectations of your stakeholders?

Knowledge Sharing and Documentation

What to coverDescribe your approach to documenting complex technical processes for others to follow. Provide an example of how you have contributed to a culture of knowledge sharing within a team.

Sharpen your knowledge

Topics to prepare

Regulated Healthcare AI Governance
Understand the specific compliance requirements for AI in healthcare, such as data privacy, auditability, and ethical guardrails. Exercise: Research HIPAA or similar regional healthcare data regulations and draft a checklist for how an AI agent should handle sensitive patient data to ensure compliance.
AI Platform Reliability and Observability
Focus on the principles of maintaining production-grade AI systems, including monitoring model performance and system health. Exercise: Design a monitoring dashboard strategy for an LLM-based agent that tracks latency, error rates, and drift in output quality.

Skills to revise

Agentic Framework Orchestration
Develop proficiency in building and managing multi-agent systems using frameworks like LangChain, AutoGen, or CrewAI. Exercise: Build a simple multi-agent workflow where one agent retrieves data and a second agent summarizes it, ensuring the orchestration logic is modular and reusable.
API Design for Infrastructure Integration
Create clean, well-documented APIs that allow infrastructure teams to consume AI capabilities easily. Exercise: Define a RESTful API specification for an AI-enabled service that includes clear request/response schemas, error handling, and authentication protocols.
AI-Assisted Development Workflow
Utilize modern AI coding assistants to accelerate development while maintaining code quality. Exercise: Use an AI coding tool to refactor a legacy script into a modular, testable library, focusing on generating comprehensive documentation and unit tests.

Your preparation checklist

Demonstrate expertise in AI orchestration and agentic frameworks
Prepare a project walkthrough showcasing how you have built or utilized agentic frameworks to automate tasks. Focus on how you handle orchestration, guardrails, and the reliability of AI-enabled components.
Prepare for healthcare-specific governance and security discussions
Research best practices for implementing security controls and governance in regulated environments. Be ready to explain how you ensure observability and compliance when deploying AI solutions in sensitive sectors.
Showcase your API-first development approach
Create a portfolio example of a reusable API, library, or template you have developed. Emphasize how your design choices made it easy for other infrastructure teams to integrate and adopt your solution.
Formulate questions regarding platform scalability and standards
Prepare thoughtful questions about how Ascendion aligns AI platform capabilities with enterprise standards and how they manage the transition from field-proven patterns to hardened, production-ready platform features.

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