Our customer is building next-generation AI-native enterprise SaaS applications that leverage advanced agentic AI, large language models (LLMs), small language models (SLMs), and multi-modal AI models to redefine finance back-office operations. As an ML Engineer, you will participate in building the architecture and technical execution, driving innovation in the FinTech AI landscape.

Machine Learning Engineer | 3-6 Y | Hyderabad (Hybrid) | Quick Joiner |

Work Mode: Hybrid (Hyderabad Office) @ T-Hub

Responsibilities

Research, fine-tune, evaluate, and deploy advanced AI/ML models, focusing on small language models (SLMs), large language models (LLMs), and multi-modal models to power agentic AI applications.

Translate models from research to production, ensuring scalability, reliability, and performance in enterprise environments.

Explore innovative use cases of SLMs, LLMs, and agentic AI for solving finance back-office challenges, such as processing emails, documents, and workflows.

Build and optimize ML data pipelines and analytics frameworks for real-time processing and decision-making.

Collaborate on NLP tasks, including tokenization, syntactic parsing, named entity recognition (NER), and embedding-based applications.

Design, build, and maintain RESTful APIs using frameworks like FastAPI, ensuring seamless integration with SaaS applications.

Develop robust unit tests alongside feature development to uphold high code quality standards.

Manage and Optimize Elasticsearch indices to support high-speed search and analytics capabilities.

Work across global teams to address engineering challenges and continuously improve systems

Required Skills: (3-6 Years)

Proficiency with PyTorch for developing and deploying AI/ML models, especially embedding representations and matrix/tensor manipulations.

Strong expertise in Python programming, including data structures, object-oriented programming (OOP), and familiarity with functional programming concepts such as generators, iterators, and decorators.

Practical experience with NLP toolkits such as NLTK, spaCy, or scikit-learn, and a solid understanding of tokenization, edit distances, NER, syntactic parsing, and related NLP concepts.

Familiarity with relational databases (e.g., MySQL) and NoSQL databases (e.g., MongoDB).

Experience with Elasticsearch, including document indexing and querying.

Familiarity with containerization technologies, including Docker and Kubernetes.

Experience with REST API development using frameworks such as FastAPI and understanding JSON structures.

Proficiency with version control tools, especially git.

Effective communication skills to collaborate with global, cross-functional teams.