Senior ML Engineer

We are seeking a highly skilled and experienced Senior Machine Learning Developer to join our dynamic team. The ideal candidate will have a strong background in machine learning (ML), deep learning (DL), and large language models (LLMs). They should be proficient in Python and possess expertise in conventional ML algorithms, DL techniques, LLM and prompt engineering.

Desire Skills

1. RAG-Based Domain-Specific Q&A System

Should have built a chatbot that answers questions from custom documents (e.g., legal contracts, company policies) using LLM + Vector DB (RAG).

2. Agentic AI Workflow Assistant (Multi-Step Planner)

Should have created an LLM agent that performs tasks like Book my flight + hotel + calendar block” via API tools using LangGraph or AutoGen.

3. Multimodal RAG for Image + Text Search

Worked on system that allows users to ask questions like “Find me the slide where person X talks about success in life” in a YouTube video or PDF deck.

4. Document Parsing Using OCR + Transformers

Extract structured data from messy PDFs using Tesseract + LayoutLM or Donut

Responsibilities:

1. Machine Learning Development:

• Design, develop, and implement ML models and algorithms to solve complex problems.
• Work on various ML projects involving data preprocessing, feature engineering, model selection, and evaluation.
• Collaborate with cross-functional teams to understand business requirements and translate them into technical solutions.

2. Deep Learning:

• Develop and deploy DL models using frameworks like TensorFlow, PyTorch, or Keras.
• Optimize DL models for performance and scalability.
• Stay updated with the latest advancements in DL and apply them to improve existing models.

3. Large Language Models:

• Develop and fine-tune large language models for various NLP tasks.
• Implement prompt engineering techniques to enhance model performance and accuracy.
• Experiment with state-of-the-art LLM architectures and methodologies.

4. Software Development:

• Write clean, maintainable, and efficient code in Python.
• Conduct code reviews and ensure adherence to best practices and coding standards.
• Implement version control using Git and collaborate with the development team through GitHub or similar platforms.

5. Data Handling:

• Work with large datasets and perform data preprocessing, cleaning, and augmentation.
• Implement data pipelines and ensure data integrity and quality throughout the ML lifecycle.

6. Research and Innovation:

• Stay abreast of the latest research and developments in the field of ML, DL, and LLMs.
• Propose and explore innovative solutions to improve model performance and address new challenges.

7. Mentorship:

• Mentor and guide junior ML engineers and team members.
• Share knowledge and best practices within the team to foster a collaborative and learning environment.