Job Description

The Role
Drive end-to-end project execution with a proactive, ownership-driven mindset.
Mentor team members and foster a collaborative, high-performing environment.
Work closely with key stakeholders to identify opportunities for innovation and deliver business value.
Stay up to date with generative AI and LLM advancements, sharing insights to guide strategy.
Build and maintain tools, libraries, and frameworks to support scalable AI development.
Collaborate cross-functionally to align goals, contribute to roadmaps, and ensure seamless system integration.
Apply strong problem-solving and communication skills to drive impactful outcomes.
Skills
Must Have Skills:
Computer Vision: Proficient in image classification, object detection, segmentation, and generation using PyTorch and pretrained models (e.g., YOLO, DETR, CLIP).
Generative AI & Open-Source LLMs: Hands-on with LangChain, LlamaIndex, PEFT, quantization, RLHF, and vector-based reasoning. Built agent-based systems and multimodal pipelines using open-source models. Strong understanding of Transformer architecture and attention mechanisms. Proficient with Hugging Face tools.
Retrieval-Augmented Generation (RAG): Deep experience in RAG pipelines using dense/sparse retrieval. Integrated LLMs with vector databases (e.g., ChromaDB, PGVector) for context-aware generation and semantic search.
Prompt Engineering:
Expert in prompt design using few-shot, zero-shot, CoT, ToT, and ReAct techniques.
Skilled in optimizing prompts for specific outcomes and enhancing LLM reasoning.
Programming & MLOps:
Proficient in Python, PyTorch, FastAPI, Docker, and Git. Experience deploying
models via REST APIs across AWS, GCP, and Azure.
ML&DL:
Strong grasp of supervised, unsupervised, and deep learning (LSTMs, RNNs, CNNs). Proficient in loss functions and regularization techniques to mitigate overfitting and improve generalization.
Good to have Skills:
Natural Language Processing (NLP):
Skilled in text preprocessing, tokenization, embeddings, and sentiment analysis. Experienced with core NLP tasks including classification, summarization, translation, and QA.
Data Engineering:
Built scalable search systems using Elasticsearch, AI search, ChromaDB, PGVector. Experience
with end-to-end data pipelines for large-scale model training and inference.

Skills Required