AI Engineering & Research Intern

Candidates typically face a timed online coding assessment focusing on data structures and Python, followed by a technical interview that probes machine‑learning fundamentals, project experience, and problem‑solving approach. The HR round assesses cultural fit, communication skills, and internship expectations. Preparing with mock coding tests and reviewing core ML concepts greatly improves success chances.

Domyn promotes a research‑centric, collaborative culture where engineers are encouraged to experiment and share knowledge. The company offers flexible working hours, mentorship programs, and a clear path for growth from intern to senior researcher, ensuring a balanced work‑life experience.

B.Tech/B.E, M.Tech, B.Sc or M.Sc in Computer Science, Artificial Intelligence, Data Science or related fields; Minimum 60% aggregate or CGPA 6.0/10; Batch 2025 (or 2026 for later intake); No active backlogs at the time of joining; Strong foundation in algorithms, data structures, and probability; Proficiency in Python programming.

Round 1: Online coding test (DSA & Python) → Round 2: Technical interview (ML concepts, project discussion) → Round 3: HR interview (fitment, motivation, internship logistics)

Domyn is a fast‑growing technology firm that focuses on building Responsible AI solutions for heavily regulated sectors such as financial services, government agencies, and heavy industry. The company’s flagship offering is a composable AI architecture that combines large language models, autonomous AI agents, and a proprietary super‑computing layer to deliver fully governable, audit‑ready AI products. With research labs in New York, Milan and India, Domyn blends cutting‑edge academic research with real‑world product delivery, helping enterprises adopt AI while staying compliant with strict regulatory frameworks. As an AI Engineering & Research Intern, you will become an integral part of the core AI team that drives innovation across Domyn’s financial‑services portfolio. You will work closely with senior researchers and engineers to design, implement, and evaluate machine‑learning models that solve domain‑specific challenges such as fraud detection, risk scoring, and market forecasting. The internship offers a hands‑on experience with large‑scale data pipelines, model monitoring, and performance optimisation, giving you a solid foundation for a future career in AI research or applied machine learning. **Key Responsibilities** 1. Implement and fine‑tune supervised and unsupervised machine‑learning models using Python libraries such as PyTorch, TensorFlow, and Scikit‑learn. 2. Design and run controlled experiments to benchmark model performance on financial datasets. 3. Analyse experiment results, generate visual reports, and recommend improvements. 4. Collaborate with data engineers to ingest, clean, and preprocess structured and time‑series financial data. 5. Evaluate model behaviour on domain‑specific tasks, including anomaly detection and predictive analytics. 6. Contribute to the development of scalable ML pipelines using tools like Airflow, Kubeflow, or Prefect. 7. Document code, experiment setups, and findings to ensure reproducibility. 8. Participate in code reviews and knowledge‑sharing sessions within the AI team. 9. Stay updated with the latest research papers and industry trends in responsible AI and finance. 10. Assist in preparing technical demos for internal stakeholders and potential clients. **Tech Stack**: Python, PyTorch/TensorFlow, Scikit‑learn, Pandas, NumPy, SQL, Airflow/Kubeflow, Git, Docker, Linux. **Growth Path**: Successful interns are often offered full‑time research or engineering roles, with opportunities to lead projects, publish papers, and work on cross‑functional AI products across Domyn’s global offices. **Why Join Domyn?** The internship provides exposure to high‑impact AI problems in finance, mentorship from seasoned researchers, and a collaborative environment that encourages curiosity and ethical AI development. You will gain practical experience building production‑grade models while contributing to responsible AI initiatives that shape industry standards.