Design, develop, fine-tune, and optimize LLMs and NLP models for applications such as AI chatbots, document understanding, customer support automation, and intelligent assistants.
Lead R&D efforts and experimentation to push the boundaries of LLM capabilities, including implementing new architectures, prompt engineering, and embedding techniques.
Apply best practices for LLM deployment, including quantization, pruning, and evaluation metrics.
Design and implement scalable data pipelines to preprocess, enrich and feed training and inference workloads for AI models.
Build connectors to integrate structured/unstructured data from databases, APIs, text files, and vector stores (e.g., FAISS, Pinecone).
Maintain data quality, labeling standards, and reproducibility of experiments.
Deploy AI models to production environments, ensuring reliability, security, and high availability.
Build and maintain APIs or microservices that expose AI capabilities to other internal teams or customer-facing platforms.
Monitor deployed models for performance drift, model health, and retraining needs.
Define project scopes, milestones, and timelines for AI initiatives.
Lead sprint planning, allocate tasks, and prioritize workloads for AI team members.
Mentor junior engineers and guide them in solving technical challenges and upskilling their AI knowledge.
Coordinate with
data scientists,
software engineers,
business analysts, and stakeholders to align on requirements and deliverables.
Document model architectures, experiments, performance benchmarks, and deployment configurations.
Deliver technical presentations and demos to internal teams and executive stakeholders.
Contribute to internal knowledge base and AI best practices documentation.
Minimum 2-3 years of experience in machine learning, with strong focus on NLP and LLMs.
Proven experience training and deploying LLMs or working with frameworks like LangChain, LlamaIndex, Hugging Face Transformers.
Solid understanding of RAG architectures, vector databases, embedding models, and contextual retrieval techniques.
Strong experience with Python, PyTorch, TensorFlow, and modern MLOps workflows.
Familiar with Cursor AI, API development, Docker/Kubernetes, RESTful services, and CI/CD pipelines.
Hands-on experience in task and team management, project planning, sprint management, and delivery oversight.
Excellent communication and presentation skills.
Good English proficiency (reading/writing/speaking).
Experience in the financial or securities domain is a plus.
