tvam Technologies Pvt Ltd
Website:
tvamapp.com
Job details:
Company Description
Founded in 2022 and headquartered in Bengaluru, tvam Technologies Private Limited builds
a socio-business platform that delivers customer-centric financial and healthcare solutions
to improve individual wellbeing and long-term stability
Role Description
We are seeking a senior, hands-on Machine Learning & LLM engineer to lead the design,
development, and scaling of foundational ML and large language model systems. The role
covers core ML theory, large-scale model architecture, LLM fine-tuning, and agentic
workflow design, with end-to-end responsibility for translating research-grade ideas into
robust, production-ready systems. This position requires strong architectural judgment,
deep technical ownership, and the ability to operate effectively across research,
engineering, and deployment.
Key Responsibilities
Fundamental ML & Core Foundation Work
- Provide technical leadership on ML fundamentals - algorithm design, optimization, generalization, statistical learning principles, and model evaluation best practices.
- Establish and evolve core infrastructure used across ML and AI systems.
- Guide implementation of robust, scalable training pipelines and workflows spanning pretraining, transfer learning, fine-tuning, and inference.
LLM Architecture, Scaling & Development
- Architect and lead development of large language model frameworks and scalable systems spanning embedding, context handling, memory, and inference pipelines.
- Influence decisions around model design, scaling laws, compute trade-offs, and training strategies (including data, objectives, regularization, etc.).
- Champion and evaluate state-of-the-art architectures, transformer variants, and production-worthy model stacks.
LLM Fine-Tuning & Personalization
- Lead fine-tuning methodologies for LLMs across diverse tasks and domains, using parameter efficient techniques (LoRA/QLoRA), prompt tuning, adapter strategies, and transfer approaches.
- Define performance metrics, evaluation workflows, and optimization processes to ensure high-quality, robust outputs in deployed systems.
Agentic Workflows & Autonomous AI Systems
- Design and implement agentic workflows and multi-agent orchestration – planning, task decomposition, tool integration, state management, and context retention frameworks.
- Integrate intelligent agents into business processes and products with an eye on reliability, safety, and end-to-end observability.
- Collaborate with cross-functional teams (Product, Research, Engineering, Cloud/Platform) to make agentic systems production-ready.
Leadership & Mentoring
- Act as a senior technical voice and mentor for engineers and applied researchers, guiding implementation quality and architectural consistency.
- Drive cross-team collaborations, design reviews, and strategic planning aligned with organizational goals.
Minimum Qualifications
- Professional experience and academic background in Machine Learning, Artificial Intelligence, Computer Science, Applied Mathematics, Statistics, or closely related fields, meeting one of the following criteria:
-
a. 0 - 2 years of relevant industry or applied research experience with a PhD from a premier institute in the above fields
b. 2+ years of relevant industry or applied research experience with a Master’s degree from a premier institute in the above fields
- Strong foundation in ML fundamentals (algorithms, optimization, generalization) and deep learning frameworks (PyTorch, TensorFlow).
- Proven experience with large language models — training, fine-tuning, evaluation, or deployment.
- Deep understanding of transformer architectures, scaling principles, and model performance trade-offs.
- Practical experience with agentic AI/agent workflows, including multi-agent or autonomous systems design.
- Solid software engineering skills: scalable platforms, microservices/APIs, MLOps, model versioning, CI/CD.
- Excellent communication and leadership skills, capable of articulating technical
vision and driving cross-team alignment.
Preferred Qualifications
- Advanced degree (MS/PhD) in Computer Science, AI/ML, Statistics, or related
discipline.
- Experience deploying large AI systems in production environments on cloud
platforms (AWS, GCP, Azure).
- Background in LLMOps, inference optimization, or distributed training at scale.
What Success Looks Like
- Clear, scalable ML foundations that serve as the bedrock of all AI/ML workflows.
- Production-ready LLM infrastructure with robust fine-tuning, evaluation, and continuous improvement pipelines.
- Agentic systems that reliably drive business outcomes with high autonomy observability, and performance.
- A strong, high-performing ML/AI team shaped and elevated through your technical mentorship and leadership
Click on Apply to know more.