Senior Software Engineer & Data Scientist

Overview Data Scientist Generative AI & NLP (Cybersecurity Products) Immediate Joiners will be Preferred. Experience: 4 to 8 Years Location: Pune Responsibilities • Design and implement GenAI and NLP solutions across multiple cybersecurity products and use cases • Build and evaluate LLM-based pipelines (RAG, embeddings, fine-tuning, prompt strategies) • Conduct applied research: experiment with new models, techniques, and architectures, and assess their product viability • Develop production-ready ML pipelines, from data preprocessing to model deployment and monitoring • Work with structured and unstructured security data (logs, alerts, threat feeds, reports) • Partner with product managers and engineers to align ML outcomes with product requirements and performance SLAs • Continuously improve model accuracy, explainability, and robustness Required Skills & Experience • 4 to 8 years of experience in Data Science / Machine Learning roles • Strong programming skills in Python with hands-on model development experience • Solid understanding of ML fundamentals (classification, clustering, anomaly detection, evaluation metrics) • Practical experience with NLP and Generative AI, including: • Transformer models and embeddings • LLM fine-tuning or adaptation techniques • Frameworks such as Hugging Face, LangChain, LlamaIndex • Experience working with large, real-world datasets and imperfect data • Ability to balance research exploration with engineering discipline • Familiarity with Responsible AI, data privacy, and governance Cybersecurity Domain Expectations • Working knowledge of core cybersecurity concepts: • Threat intelligence, malware, phishing, endpoint/network security • Familiarity with security data and standards: • IOCs, MITRE ATT&CK, STIX/TAXII, SIEM data • Understanding that security ML systems must prioritize: • Low false positives • Explainability • Resilience to adversarial behavior Enterprise & Product Mindset • Experience contributing to productized ML systems, not just notebooks • Understanding of model lifecycle management, versioning, and monitoring • Comfort working in cross-team, multi-product environments • Ability to document research findings and technical decisions clearly