New opportunity

Sr. AI/ML Engineer

Dailoqa · Noida, India

About this role

Dailoqa is seeking a Senior AI/ML Engineer to join their early-stage venture. The role focuses on building scalable, multi-modal LLM-based AI agents and conducting research to solve complex AI problems. Key Responsibilities: Develop agentic AI using frameworks like LangGraph, Microsoft Autogen, or Crewai. Conduct research in RAG, semantic search, knowledge representation, tool usage, fine-tuning, and LLM reasoning. Automate model deployment, monitoring, and retraining processes while collaborating with data scientists to optimize code. Implement version control and governance for models and data. Build datasets for training and evaluation. Required Qualifications: Bachelor's degree in Computer Science, Software Engineering, or a related field. 5-8 years of experience in MLOps, DevOps, or related roles. Strong programming proficiency in Python and deep learning frameworks such as PyTorch, JAX, or TensorFlow. Solid understanding of machine learning concepts and cloud platforms (AWS, Azure, or GCP) with infrastructure-as-code tools like Terraform. Desired Skills: Experience with experiment tracking, model versioning, data pipeline orchestration (Apache Airflow or Prefect), and software testing practices. Understanding of ethical considerations in machine learning deployments. Candidates should be customer-focused, adaptable, and thrive in a fast-paced environment.

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

Skills for this role

PythonLangGraphMicrosoft AutogenCrewaiRAGSemantic SearchKnowledge RepresentationFine-tuningLlamaIndexLangChainPyTorchHuggingFaceFastAPIPostgresSQLAlchemyAlembicOpenAIDockerAzureTypescriptReactMLOpsDevOpsJAXTensorflowTerraformAWSGCPApache AirflowPrefect

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

Development of Scalable Agentic AI
The core mandate is to build multi-modal LLM-based agents using frameworks like LangGraph, Autogen, or Crewai. Success requires moving beyond simple prompt engineering to architecting autonomous systems that can handle complex tool usage, reasoning, and multi-step workflows at scale.
End-to-End MLOps and Infrastructure Automation
With a requirement for 5-8 years of MLOps/DevOps experience, the role prioritizes the automation of the entire model lifecycle. This includes implementing infrastructure-as-code via Terraform, managing model deployment, monitoring, retraining pipelines, and enforcing strict version control and governance for both models and datasets.
Applied AI Research and Dataset Engineering
The role demands a balance between research and production, specifically in RAG, semantic search, and knowledge representation. A critical priority is the ability to build high-quality datasets for training and evaluation, ensuring that research innovations translate into measurable improvements in model performance.

Likely challenges to think through

Balancing Early-Stage Agility with Governance
As an early-stage venture, the company likely requires rapid iteration, yet the role explicitly demands formal model governance and version control. The challenge is to implement robust MLOps practices that provide necessary structure without stifling the speed of development required in a startup environment.
Complexity of Multi-Modal Agent Orchestration
Building agents that effectively utilize tools and reason across multi-modal inputs is technically difficult. Candidates must be prepared to discuss the trade-offs between different agentic frameworks and how to handle the non-deterministic nature of LLM outputs in a production-grade, customer-facing system.

Practice your answers

Technical practice

Architecting Multi-modal AI Agents

What to coverDiscuss the trade-offs between using frameworks like LangGraph, Microsoft Autogen, and Crewai for building scalable agentic systems. Explain how you handle state management and tool-use orchestration in multi-modal environments.

Optimizing RAG and Semantic Search

What to coverDescribe your approach to improving retrieval accuracy in RAG pipelines. Address how you manage knowledge representation and handle context window limitations when integrating diverse data sources.

LLM Fine-tuning and Reasoning

What to coverExplain your methodology for fine-tuning LLMs for specific domain tasks versus prompt engineering. Discuss techniques for enhancing model reasoning capabilities and evaluating performance improvements.

MLOps and Model Lifecycle Management

What to coverDetail your strategy for automating model deployment, monitoring, and retraining. Suggest how to implement robust version control and governance for both model weights and training datasets.

Infrastructure as Code and Cloud Scalability

What to coverExplain how you utilize Terraform to manage cloud infrastructure (AWS, Azure, or GCP) for AI workloads. Discuss best practices for ensuring reproducibility and scalability in your deployment environments.

Data Pipeline Orchestration

What to coverCompare the use of Apache Airflow versus Prefect for managing complex machine learning workflows. Explain how you ensure data quality and pipeline reliability in production.

Deep Learning Framework Proficiency

What to coverDiscuss your experience transitioning between PyTorch, JAX, and TensorFlow. Explain how you choose the appropriate framework based on specific project requirements and performance needs.

Software Engineering Practices in AI

What to coverDescribe how you integrate software testing and CI/CD practices into machine learning projects. Suggest how to collaborate with data scientists to refactor and optimize code for production readiness.

Behavioural practice

Thriving in Early-Stage Ventures

What to coverDescribe a situation where you had to operate with significant ambiguity in a fast-paced environment. Explain how you prioritized tasks to deliver value while maintaining high technical standards.

Customer-Centric Problem Solving

What to coverProvide an example of how you translated a complex customer business problem into a technical AI solution. Focus on how you ensured the final output met the user's specific needs.

Navigating Ethical Considerations

What to coverDiscuss a time you identified an ethical risk in a machine learning deployment. Explain the actions you took to mitigate the risk and how you communicated these concerns to stakeholders.

Cross-functional Collaboration

What to coverDescribe a scenario where you had to collaborate with data scientists or other engineers to resolve a technical bottleneck. Detail your communication approach and the resulting impact on the project.

Sharpen your knowledge

Topics to prepare

Agentic AI Architectures
Focus on the design patterns for multi-modal LLM agents using frameworks like LangGraph or AutoGen. Exercise: Draft a system architecture diagram for an agent that uses tool-calling to perform multi-step reasoning tasks, ensuring you account for state management and error handling.
Advanced RAG and Knowledge Representation
Deepen understanding of retrieval-augmented generation beyond basic vector search. Exercise: Compare and contrast graph-based knowledge representation with standard vector embeddings for improving LLM reasoning accuracy in complex domain-specific queries.
MLOps and Model Governance
Study the lifecycle of LLM deployment, including monitoring and retraining. Exercise: Outline a CI/CD pipeline strategy that incorporates automated model evaluation and version control for both datasets and model weights.

Skills to revise

Infrastructure as Code (Terraform)
Practical application of IaC for cloud environments. Exercise: Write a Terraform script to provision a scalable cloud infrastructure (AWS or Azure) capable of hosting a containerized FastAPI application and a vector database.
Data Pipeline Orchestration
Proficiency in Apache Airflow or Prefect for data workflows. Exercise: Create a DAG in Airflow that automates the ingestion, cleaning, and vectorization of a raw dataset for use in an LLM training or evaluation pipeline.
Deep Learning Frameworks
Hands-on implementation with PyTorch or JAX. Exercise: Implement a custom training loop in PyTorch that includes experiment tracking and model checkpointing, specifically focusing on fine-tuning a small language model.

Your preparation checklist

Prepare a technical walkthrough of an agentic AI project
Select a project where you implemented multi-modal LLM agents using frameworks like LangGraph, Autogen, or Crewai. Be prepared to explain your architectural choices, how you handled tool usage, and the specific reasoning patterns you implemented to solve a business problem.
Document your experience with RAG and knowledge representation
Create a summary of your work with Retrieval-Augmented Generation (RAG) and semantic search. Focus on how you structured data for retrieval, the specific vector databases or knowledge representation techniques used, and how you evaluated the accuracy and relevance of the generated responses.
Outline your MLOps and infrastructure-as-code workflow
Prepare a detailed explanation of how you have automated model deployment, monitoring, and retraining. Highlight your experience using Terraform for infrastructure, Apache Airflow or Prefect for orchestration, and your approach to version control for both models and datasets.
Review your experience with deep learning frameworks and optimization
Be ready to discuss your proficiency in PyTorch, JAX, or TensorFlow. Prepare examples of how you have refactored or optimized machine learning code for production environments and your experience with fine-tuning LLMs for specific tasks.
Formulate questions about Dailoqa's early-stage AI roadmap
Prepare thoughtful questions regarding the company's current challenges in scaling their AI agents, their approach to ethical AI governance, and how the engineering team balances rapid research innovation with stable production deployment.

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

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