New opportunity

Engineering Lead ( AI/ML, Gen AI)

Algoleap Technologies Pvt Ltd · Hyderabad, India

About this role

The Engineering Lead (AI/ML, Gen AI) at Algoleap Technologies will design and build AI solutions to address diverse business problems, ranging from document and image extraction to predictive models and LLM-based agents. Responsibilities include applying AI engineering best practices, enforcing model governance, building system observability, and managing token economics and compute costs. The role requires mentoring engineers, setting technical standards, and communicating AI capabilities to non-technical stakeholders. Key Responsibilities: Design and build AI solutions; Apply best practices in prompt design, retrieval, and evaluation; Enforce model governance and risk assessment; Implement logging, tracing, and monitoring; Manage compute costs and model performance metrics; Mentor the engineering team. Required Qualifications: 8+ years of software engineering experience, with at least 3+ years in applied AI/ML in production; Proficiency in Python and standard AI/ML tooling; Experience with model governance, observability, and cost management; Proven track record of technical leadership and mentorship; Strong business acumen and communication skills. Preferred Qualifications: Experience with document/image classification, dashboarding tools (Power BI, Tableau, Grafana), prompt versioning, and exposure to data-intensive industries like finance or healthcare.

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

Skills for this role

PythonMachine LearningGenerative AILLMAI AgentsComputer VisionModel GovernanceObservabilityToken EconomicsPrompt EngineeringRetrieval StrategiesVector DatabasesModel EvaluationRegression TestingDashboarding

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

Production-Grade AI Governance and Observability
The role requires establishing rigorous standards for model deployment, including approval workflows, risk/bias assessments, and comprehensive logging/tracing. You must demonstrate how you build systems that surface failures before they impact business operations, ensuring reliability in production environments.
Economic AI Engineering and Cost Management
A core priority is managing token economics and compute costs. You must be prepared to discuss how you balance model accuracy against operational spend, including selecting appropriate model sizes and optimizing retrieval strategies to ensure AI solutions remain financially viable.
Technical Leadership and Stakeholder Translation
You are expected to mentor engineers while acting as a bridge to non-technical leadership. This involves setting technical standards for the team and clearly communicating the capabilities and limitations of AI systems to ensure realistic business expectations.

Likely challenges to think through

Balancing Innovation with Pragmatic Problem Solving
The role emphasizes choosing the right tool for the problem rather than following trends. The challenge is to demonstrate the discipline to recommend simpler, non-AI solutions when appropriate, requiring strong business acumen to justify technical decisions to stakeholders.
Managing Complexity in Model Evaluation and Regression
Implementing robust evaluation frameworks and regression testing for LLM-based agents is inherently difficult due to the non-deterministic nature of generative models. Prepare by discussing how you maintain version control for prompts and models to ensure consistent performance over time.

Practice your answers

Technical practice

Designing AI Solutions for Business Problems

What to coverDiscuss your process for selecting between LLMs, classical ML, or computer vision for a specific business requirement. Focus on evaluating trade-offs like accuracy, latency, and cost rather than choosing the most popular technology.

Implementing AI Observability

What to coverExplain how you design logging and tracing for LLM-based agents. Describe how you monitor for failure modes, drift, and latency to ensure issues are identified before impacting business operations.

Managing Token Economics and Compute Costs

What to coverDetail your strategy for optimizing LLM costs at scale. Discuss techniques like model selection based on task complexity, caching, or prompt optimization to balance performance with budget constraints.

Model Governance and Risk Assessment

What to coverDescribe your framework for moving models into production. Address how you implement approval workflows, bias assessment, and documentation to ensure compliance and reliability.

Prompt Engineering and Evaluation Frameworks

What to coverExplain how you manage prompt versioning and regression testing. Suggestion: Discuss how you build automated evaluation pipelines to ensure consistent model outputs after prompt changes.

Retrieval Strategies for LLMs

What to coverDiscuss your experience with vector databases and retrieval-augmented generation (RAG). Explain how you optimize retrieval accuracy to reduce hallucinations in LLM-based agents.

Performance Metrics Dashboarding

What to coverDescribe the key metrics you track for production AI systems, such as throughput, error rates, and accuracy. Suggestion: Mention how you use tools like Grafana or Tableau to visualize these metrics for stakeholders.

Document and Image Extraction

What to coverExplain your approach to building robust pipelines for document or image classification. Discuss how you handle edge cases and ensure high extraction accuracy in production environments.

Behavioural practice

Mentoring and Technical Leadership

What to coverProvide an example of how you have mentored junior engineers on AI best practices. Focus on how you set technical standards and improved the team's overall ability to ship reliable AI systems.

Communicating AI to Non-Technical Stakeholders

What to coverDescribe a time you had to explain the limitations or capabilities of an AI system to business leadership. Focus on how you translated technical trade-offs into clear business outcomes.

Choosing Simpler Solutions Over AI

What to coverGive an example of a project where you determined that a non-AI solution was more appropriate than an AI-based one. Explain your reasoning and how you communicated this to the team.

Handling Production AI Failures

What to coverDescribe a situation where an AI system failed in production. Explain your process for diagnosing the root cause, implementing a fix, and preventing future occurrences through better observability or governance.

Sharpen your knowledge

Topics to prepare

AI Model Governance and Risk Assessment
Review frameworks for model approval workflows and bias assessment. Exercise: Draft a standard operating procedure for promoting a model to production, including documentation requirements for risk and bias mitigation, as this is a core requirement for the Engineering Lead role.
Token Economics and Compute Cost Management
Study strategies for optimizing LLM inference costs, including model size selection and throughput management. Exercise: Create a cost-benefit analysis template that compares different model sizes against latency and accuracy requirements for a hypothetical document extraction task.
AI System Observability
Focus on logging, tracing, and monitoring for production AI systems. Exercise: Design a monitoring architecture that tracks input/output drift, latency, and failure modes, ensuring these metrics are surfaced to stakeholders via a dashboard.

Skills to revise

Prompt Engineering and Evaluation Frameworks
Practice versioning prompts and implementing regression testing for LLM outputs. Exercise: Build a small evaluation pipeline using a test set of inputs to measure output consistency and accuracy when changing prompt versions.
Dashboarding for AI Metrics
Gain proficiency in visualizing model performance metrics like accuracy, drift, and cost. Exercise: Use a tool like Grafana or Power BI to create a mock dashboard that displays real-time throughput and error rates for an AI agent.
Technical Mentorship and Communication
Refine the ability to explain complex AI trade-offs to non-technical stakeholders. Exercise: Prepare a 5-minute presentation explaining the limitations of a specific LLM-based agent to a hypothetical non-technical business leader.

Your preparation checklist

Prepare a production-grade AI project walkthrough
Select a past project where you deployed an AI/ML solution. Prepare to discuss the full lifecycle, specifically focusing on how you implemented observability, logging, and tracing to monitor model performance and latency in production.
Develop a framework for AI model governance
Draft a brief document or presentation outlining your approach to model governance, including how you handle risk assessment, bias detection, and approval workflows before promoting a model to production.
Create a cost-management and metrics dashboard plan
Prepare a mock-up or description of a dashboard you would build to track token economics, compute costs, and performance metrics like drift and error rates. Explain how you would use this data to optimize model selection.
Practice explaining technical trade-offs to non-technical stakeholders
Prepare a scenario where you had to explain the limitations of an LLM or AI agent to a business leader. Focus on translating technical constraints into business outcomes and managing expectations regarding accuracy and cost.
Formulate questions about Algoleap's AI infrastructure
Prepare thoughtful questions for the interviewers regarding their current stack, such as: 'How does the team currently handle prompt versioning and regression testing?' or 'What are the primary challenges you face in balancing model accuracy with compute costs at your current scale?'

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

KEEP EXPLORING

Similar AI jobs

Related skills and specializations in India. Matched to this role, not your profile.

Explore more jobs

Finding similar opportunities…