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Associate Engineer – AI, Data & Digital Engineering

Google

  • Hyderabad, Telangana
  • β‚Ή4.2 LPA
  • Fresher (0-1 Yrs)
  • Posted Today
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About the role

Agivant Technologies is a fast‑growing product engineering firm that partners with leading ed‑tech and technology platforms to deliver AI‑driven solutions. Headquartered in Hyderabad, the company has built a reputation for leveraging cutting‑edge machine learning, generative AI, and data engineering to solve real‑world business problems. With a culture that encourages continuous learning, Agivant invests heavily in upskilling its talent through collaborations with training partners such as NxtWave, ensuring that fresh engineers are ready to contribute from day one. The role of Associate Engineer – AI, Data & Digital Engineering is designed for recent graduates who are passionate about artificial intelligence, data pipelines, and full‑stack development. As an entry‑level engineer, you will work alongside senior data scientists, software architects, and product managers to design, develop, and deploy intelligent services that power Agivant’s client offerings. The position offers exposure to a wide spectrum of technologiesβ€”from core programming languages and data structures to cloud platforms and generative AI modelsβ€”making it an ideal launchpad for a career in AI and software engineering. **Key Responsibilities** 1. Develop clean, efficient code in Python, Java, C++ or JavaScript to implement AI and data processing modules. 2. Design and optimise data structures, including knowledge graphs, to support complex query and inference workloads. 3. Build and maintain SQL queries and database schemas for reliable data storage and retrieval. 4. Participate in end‑to‑end development of data pipelines, integrating ETL processes with cloud services. 5. Collaborate with cross‑functional teams to define API contracts and implement RESTful services. 6. Conduct unit testing, debugging, and performance profiling to ensure high‑quality deliverables. 7. Contribute to version‑control workflows using Git and follow CI/CD best practices. 8. Assist in the creation of documentation, technical specifications, and project demos for stakeholders. 9. Stay updated with emerging AI trends such as generative models, RAG, and prompt engineering, and propose proof‑of‑concepts. 10. Participate actively in hackathons, open‑source contributions, and internal tech talks to foster a culture of innovation. **Tech Stack**: Python, Java, C++, JavaScript, Data Structures & Algorithms, OOP, Knowledge Graphs, SQL, Git, REST APIs, AWS/GCP/Azure, Docker, Linux, CI/CD, MLflow, Generative AI frameworks (Claude, Gemini), Graph Neural Networks. **Growth Path**: Starting as an Associate Engineer, high performers can progress to Senior Engineer, Lead Engineer, and eventually AI/ML Architect roles within 3‑5 years, with opportunities to lead product modules and mentor new hires. **Why Join Agivant**: The company offers a competitive CTC of β‚Ή4.2β€―LPA, a structured 2‑year service agreement that guarantees exposure to high‑impact projects, and a vibrant learning environment backed by NxtWave’s curriculum. Freshers get hands‑on experience with industry‑grade AI tools, cloud infrastructure, and real client engagements, positioning them for rapid career advancement in the booming AI sector.

Eligibility & process

Who can apply

Degree: B.Tech/B.E., M.Tech/M.E., MCA, BCA. Branches: Computer Science (CSE), Information Technology (IT), MCA, Electronics & Communication (ECE), BCA. Academic: Minimum 80% in 10th, Intermediate, and Degree. Eligible batches: 2025 and 2026 pass‑outs. No active backlogs allowed at the time of joining. Must be willing to serve a non‑negotiable 2‑year service agreement and relocate to Hyderabad for the offline recruitment drive.

Skills & stack

Python Java C++ JavaScript Data Structures & Algorithms Object Oriented Programming Knowledge Graphs

Selection process

  1. Round 1: Company Screening
  2. Round 2: Offline Assessment / Coding Round
  3. Round 3: Technical Interview –
  4. Round 1
  5. Round 4: Technical Interview –
  6. Round 2
  7. Round 5: Managerial Round
  8. Round 6: HR Round

Prep kit

19 resources
Study material 4
Company-specific guides 4
Likely coding questions 4
Round-wise strategy 4
Round 1: Online Assessment (OA) Aptitude, Quantitative Logic & 2 Coding Problems Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1 Core Tech Stack (Python, Java, C++, JavaScript, Data Structures &, Algorithms, Object Oriented Programming, Knowledge Graphs) & Live Code Tracing Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving Database Schemas, APIs & Architecture Basics Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion Google Core Values, Learning Agility & Offer Terms Demonstrate passion, strong communication, and readiness for full-time collaboration.
Technical questions 4
What is the bias-variance tradeoff and how do you prevent overfitting? Answer
Model answer: High bias leads to underfitting (oversimplified model), high variance leads to overfitting (captures noise). Mitigate using L1/L2 Regularization, Dropout, Cross-Validation, and data augmentation.
Explain the difference between Precision, Recall, and F1-Score. Answer
Model answer: Precision = TP / (TP + FP) (correctness of positive predictions). Recall = TP / (TP + FN) (coverage of actual positives). F1-Score is the harmonic mean of Precision and Recall.
How does Gradient Descent work and what is the role of Learning Rate? Answer
Model answer: It optimizes loss functions by iteratively moving weights in the direction of negative gradient. A large learning rate may overshoot the minimum; a small rate causes slow convergence.
What is the difference between Supervised, Unsupervised, and Self-Supervised learning? Answer
Model answer: Supervised uses labeled data (X -> y). Unsupervised finds hidden patterns in unlabeled data (clustering/PCA). Self-supervised generates labels from input data (e.g. masked language modeling in BERT/Transformers).
Behavioural & HR questions 3
Why do you want to join Google as a Associate Engineer – AI, Data & Digital Engineering?
Highlight Google's market reputation, recent tech innovations, and how your skills in Python directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
Google β‚Ή4.2 LPA
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