Why This Job is Featured on The SaaS Jobs
This Data Science Manager role stands out in the SaaS landscape because it centers on taking foundation models from research-grade capability to domain-specific, production-ready systems. The emphasis on PEFT methods, large-scale training, and evaluation discipline reflects the direction many SaaS products are moving as generative AI becomes a core feature rather than an add-on. With on-site delivery in Bengaluru, it also signals a role embedded closely with engineering and product execution rather than a purely advisory remit.
From a SaaS career perspective, the work maps to durable problems: building repeatable ML pipelines, managing model lifecycle, and balancing performance, cost, and reliability in real deployments. Exposure to distributed training, cloud ML platforms, and MLOps practices builds experience that transfers across SaaS companies operating at different stages, especially those operationalising LLMs for customer-facing workflows. Leading applied scientists and ML engineers also strengthens the ability to turn experimentation into roadmap-impacting outcomes.
The role fits professionals who enjoy hands-on technical depth while setting direction for others, and who are comfortable translating ambiguity into measurable model improvements. It will suit someone motivated by applied AI delivery, cross-functional collaboration, and the craft of making advanced models dependable in production environments.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
Technical Expertise
- Strong background in machine learning, deep learning, and NLP, with proven experience in training and fine-tuning large-scale models (LLMs, transformers, diffusion models, etc.).
- Hands-on expertise with Parameter-Efficient Fine-Tuning (PEFT) approaches such as LoRA, prefix tuning, adapters, and quantization-aware training.
- Proficiency in PyTorch, TensorFlow, Hugging Face ecosystem and good to have distributed training frameworks (e.g., DeepSpeed, PyTorch Lightning, Ray).
- Basic understanding of MLOps best practices, including experiment tracking, model versioning, CI/CD for ML pipelines, and deployment in production environments.
- Experience working with large datasets, feature engineering, and data pipelines, leveraging tools such as Spark, Databricks, or cloud-native ML services (AWS Sagemaker, GCP Vertex AI or Azure ML).
- Knowledge of GPU/TPU optimization, mixed precision training, and scaling ML workloads on cloud or HPC environments.
- Applied Problem-Solving
Mandatory skill -
- Demonstrated success in adapting foundation models to domain-specific applications through fine-tuning or transfer learning.Mandatory skill -
- Strong ability to design, evaluate, and improve models using robust validation strategies, bias/fairness checks, and performance optimization techniques.
- Experience in working on applied AI problems across NLP, computer vision, or multimodal systems or any other domain.
Leadership & Collaboration
- Proven ability to lead and mentor a team of applied scientists and ML engineers, providing technical guidance and fostering innovation.
- Strong cross-functional collaboration skills to work with product, engineering, and business stakeholders to deliver impactful AI solutions.
- Ability to translate cutting-edge research into practical, scalable solutions that meet real-world business needs.
Other
- Excellent communication and presentation skills to articulate complex ML concepts to both technical and non-technical audiences.
- Continuous learner with awareness of emerging trends in generative AI, foundation models, and efficient ML techniques.
Education & Experience
- Master’s or Ph.D. in Computer Science, Machine Learning, Data Science, Statistics, or a related field.
- 7+ years of hands-on experience in applied machine learning and data science, with at least 2+ years in a leadership or managerial role.
