Zensar is seeking an experienced Agentic Engineer to join their team in Pune. This role focuses on designing, developing, and implementing innovative AI-powered solutions, with a specific emphasis on generative AI techniques and agentic systems. Responsibilities include collaborating with cross-functional teams to translate business requirements into technical solutions, optimizing AI algorithms for performance and accuracy, conducting testing and debugging, and integrating AI solutions into existing software architectures. The role also involves documenting technical projects, providing guidance to junior team members, and ensuring compliance with industry standards. Qualifications include a Bachelor's degree in Computer Science, Engineering, or a related field with a strong focus on AI and machine learning. Candidates must have 5 to 8 years of experience in software development, including a proven track record in AI application development. Proficiency in Python, Java, or C++ is required, along with in-depth knowledge of machine learning frameworks such as TensorFlow and PyTorch. Experience in natural language processing, computer vision, or other AI-specific domains is preferred. The ideal candidate possesses strong problem-solving and analytical skills, excellent communication abilities, and a self-driven approach to staying updated with AI trends. This is an on-site position based in Pune, India.
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Skills for this role
PythonJavaC++TensorFlowPyTorchGenerative AIMachine LearningNatural Language ProcessingComputer VisionSoftware Development
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Get interview-ready for this role
A focused preparation guide, built around this job’s responsibilities and requirements.
✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01
Understand the role
What matters most
Development of Agentic AI Systems
The core focus is designing and implementing generative AI and agentic systems. You must demonstrate the ability to move beyond static models to create autonomous or semi-autonomous agents that can execute complex workflows, requiring a deep understanding of AI orchestration and decision-making logic.
Integration into Existing Architectures
A critical requirement is the seamless integration of AI solutions into established software environments. You will need to balance the experimental nature of AI development with the stability and performance requirements of production-grade enterprise software.
Technical Leadership and Mentorship
With 5-8 years of experience, you are expected to provide technical guidance to junior team members. This involves not only individual coding contributions but also documenting projects and fostering a collaborative environment to ensure knowledge transfer across the team.
Likely challenges to think through
Balancing Innovation with Enterprise Constraints
The role requires staying at the forefront of rapidly evolving AI trends while adhering to industry standards and existing software architectures. Prepare by discussing how you manage technical debt while introducing cutting-edge generative AI features into legacy or stable systems.
Managing Complexity in Agentic Workflows
Agentic systems often face challenges regarding reliability, debugging, and non-deterministic behavior. Prepare by highlighting your experience in rigorous testing methodologies and your approach to monitoring and optimizing AI performance in production environments.
03
Practice your answers
Technical practice
Designing Agentic Systems
What to coverExplain your approach to designing autonomous agentic systems. Discuss how you handle task decomposition, memory management, and tool-use capabilities within a generative AI framework.
Optimizing AI Model Performance
What to coverDescribe your methodology for optimizing AI algorithms for both latency and accuracy. Mention specific techniques you have used to improve model inference speed in production environments.
Framework Proficiency: TensorFlow vs. PyTorch
What to coverCompare the strengths and weaknesses of TensorFlow and PyTorch for building agentic applications. Suggestion: Prepare to discuss why you might choose one over the other for a specific project requirement.
Integrating AI into Existing Architectures
What to coverHow do you approach the integration of AI models into legacy software systems? Focus on API design, data pipeline compatibility, and ensuring seamless functionality without disrupting existing services.
Testing and Debugging AI Systems
What to coverWhat is your strategy for testing non-deterministic AI models? Discuss how you implement unit testing, integration testing, and monitoring for drift or performance degradation in production.
NLP and Computer Vision Application
What to coverProvide an example of how you have applied NLP or computer vision techniques to solve a business problem. Focus on the specific architecture used and the challenges faced during implementation.
Compliance and Best Practices
What to coverHow do you ensure your AI development adheres to industry standards and ethical guidelines? Discuss your process for documentation and maintaining transparency in AI decision-making.
Managing Technical Debt in AI Projects
What to coverHow do you balance the need for rapid experimentation with the requirement for maintainable, production-grade code? Suggestion: Highlight your approach to modularizing AI components.
Behavioural practice
Mentoring Junior Engineers
What to coverDescribe a time you provided technical guidance to a junior team member. Explain how you balanced their learning needs with the project's delivery timelines.
Cross-Functional Collaboration
What to coverGive an example of translating complex business requirements into a technical AI solution. How did you ensure stakeholders understood the technical trade-offs involved?
Handling Ambiguity and Rapid Change
What to coverThe AI field evolves quickly; describe a situation where you had to adapt to a new technology or methodology on short notice. How did you manage the learning curve while maintaining productivity?
Managing Multiple Projects
What to coverDescribe your process for prioritizing tasks when managing multiple AI projects simultaneously. How do you ensure quality and compliance across all workstreams?
02
Sharpen your knowledge
Topics to prepare
Agentic System Architecture
Focus on the design patterns for autonomous agents, specifically how to manage state, memory, and tool-use orchestration. Exercise: Draft a high-level architecture diagram for an agent that can autonomously research a topic and summarize findings, focusing on how the agent decides which tools to invoke. This is relevant to the role's core focus on agentic systems.
Generative AI Integration Patterns
Understand how to integrate LLMs into existing software architectures, including handling latency, cost, and output reliability. Exercise: Review common patterns for RAG (Retrieval-Augmented Generation) and evaluate how to integrate these into a legacy system without disrupting existing workflows. This aligns with the requirement to integrate AI solutions into existing software.
Skills to revise
Framework Proficiency (PyTorch/TensorFlow)
Demonstrate deep technical knowledge of model training and deployment pipelines. Exercise: Build a small-scale model using either PyTorch or TensorFlow that performs a specific NLP task, ensuring you document the data preprocessing and model evaluation steps. This directly addresses the requirement for in-depth knowledge of these frameworks.
Algorithm Optimization
Focus on improving the performance and accuracy of AI models in production environments. Exercise: Take a baseline model and implement techniques such as quantization or pruning to reduce inference time while maintaining accuracy. This is essential for the responsibility of optimizing AI algorithms for performance.
Technical Documentation and Communication
Practice translating complex AI concepts into clear, actionable technical documentation for cross-functional teams. Exercise: Write a technical design document for an AI feature, including API specifications, error handling strategies, and integration points. This is relevant to the role's requirement for documenting projects and collaborating with diverse teams.
04
Your preparation checklist
Prepare an Agentic Workflow Case Study
Select a past project where you designed an autonomous agent or generative AI system. Prepare to explain your architectural choices, the specific frameworks used (e.g., LangChain, AutoGPT, or custom implementations), and how you optimized the agent's decision-making loop for performance and accuracy.
Demonstrate Framework Proficiency
Review your experience with TensorFlow and PyTorch. Be ready to discuss a specific instance where you had to debug a model or optimize an algorithm for production, highlighting your ability to integrate these models into existing software architectures.
Articulate Cross-Functional Collaboration
Prepare examples of how you have translated complex business requirements into technical AI specifications. Focus on your communication strategy when explaining AI limitations or performance trade-offs to non-technical stakeholders.
Formulate Technical Questions for Zensar
Develop thoughtful questions regarding Zensar's current AI stack, their approach to agentic system governance, and how the team balances rapid experimentation with the need for robust, production-grade software documentation and compliance.
Review Mentorship and Leadership Experience
Since the role requires guiding junior members, prepare a brief narrative on your mentorship style, including how you have previously conducted code reviews, shared best practices, or facilitated knowledge transfer within a technical team.
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