Associate Engineer β AI, Data & Digital Engineering
About the role
Agivant Technologies is a fastβgrowing product engineering firm that partners with leading edβtech and technology platforms to deliver AIβdriven solutions. Headquartered in Hyderabad, the company has built a reputation for leveraging cuttingβedge machine learning, generative AI, and data engineering to solve realβworld 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 β AI, Data & Digital Engineering is designed for recent graduates who are passionate about artificial intelligence, data pipelines, and fullβstack development. As an entryβlevel engineer, you will work alongside senior data scientists, software architects, and product managers to design, develop, and deploy intelligent services that power Agivantβs client offerings. The position offers exposure to a wide spectrum of technologiesβfrom core programming languages and data structures to cloud platforms and generative AI modelsβmaking it an ideal launchpad for a career in AI and software engineering.
Key Responsibilities
- Develop clean, efficient code in Python, Java, C++ or JavaScript to implement AI and data processing modules.
- Design and optimise data structures, including knowledge graphs, to support complex query and inference workloads.
- Build and maintain SQL queries and database schemas for reliable data storage and retrieval.
- Participate in endβtoβend development of data pipelines, integrating ETL processes with cloud services.
- Collaborate with crossβfunctional teams to define API contracts and implement RESTful services.
- Conduct unit testing, debugging, and performance profiling to ensure highβquality deliverables.
- Contribute to versionβcontrol workflows using Git and follow CI/CD best practices.
- Assist in the creation of documentation, technical specifications, and project demos for stakeholders.
- Stay updated with emerging AI trends such as generative models, RAG, and prompt engineering, and propose proofβofβconcepts.
- Participate actively in hackathons, openβsource 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 3β5 years, with opportunities to lead product modules and mentor new hires.
Why Join Agivant: The company offers a competitive CTC of βΉ4.2 LPA, a structured 2βyear service agreement that guarantees exposure to highβimpact projects, and a vibrant learning environment backed by NxtWaveβs curriculum. Freshers get handsβon experience with industryβgrade 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 passβouts. No active backlogs allowed at the time of joining. Must be willing to serve a nonβnegotiable 2βyear service agreement and relocate to Hyderabad for the offline recruitment drive.
Skills & stack
Selection process
- Round 1: Company Screening
- Round 2: Offline Assessment / Coding Round
- Round 3: Technical Interview β
- Round 1
- Round 4: Technical Interview β
- Round 2
- Round 5: Managerial Round
- Round 6: HR Round
Prep kit
19 resourcesStudy material 4
Company-specific guides 4
Likely coding questions 4
Round-wise strategy 4
Technical questions 4
What is the bias-variance tradeoff and how do you prevent overfitting? Answer
Explain the difference between Precision, Recall, and F1-Score. Answer
How does Gradient Descent work and what is the role of Learning Rate? Answer
What is the difference between Supervised, Unsupervised, and Self-Supervised learning? Answer
Behavioural & HR questions 3
Highlight Google's market reputation, recent tech innovations, and how your skills in Python directly solve their team's objectives.
Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.