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The design and development of a cutting-edge application powered by large language models (LLMs).
This tool will provide market analysis and generate high-quality, data-driven periodic insights.
You will play a critical role in building a scalable and intelligent system that integrates structured data, NLP capabilities, and domain-specific knowledge to produce analyst-grade content.
Key Responsibilities
- Design and develop LLM-based systems for automated market analysis.
- Build data pipelines to ingest, clean, and structure data from multiple sources (e.g., market feeds, news articles, technical reports, internal datasets).
- Fine-tune or prompt-engineer LLMs (e.g., GPT-4.5, Llama, Mistral) to generate concise, insightful reports.
- Collaborate closely with domain experts to integrate industry-specific context and validation into model outputs.
- Implement robust evaluation metrics and monitoring systems to ensure quality, relevance, and accuracy of generated insights.
- Develop and maintain APIs and/or user interfaces to enable analysts or clients to interact with the LLM system.
- Stay up to date with advancements in the GenAI ecosystem and recommend relevant improvements or integrations.
- Participate in code reviews, experimentation pipelines, and collaborative research Required :
- Strong fundamentals in machine learning, deep learning, and natural language processing (NLP).
- Proficiency in Python, with hands-on experience using libraries such as NumPy, Pandas, and Matplotlib/Seaborn for data analysis and visualization.
- Experience developing applications using LLMs (both closedand open-source models).
- Familiarity with frameworks like Hugging Face Transformers, LangChain, LlamaIndex, etc.
- Experience building ML models (e.g., Random Forest, XGBoost, LightGBM, SVMs), along with familiarity in training and validating models.
- Practical understanding of deep learning frameworks: TensorFlow or PyTorch.
- Knowledge of prompt engineering, Retrieval-Augmented Generation (RAG), and LLM evaluation strategies.
- Experience working with REST APIs, data ingestion pipelines, and automation workflows.
- Strong analytical thinking, problem-solving skills, and the ability to convert complex technical work into business-relevant insights.
- Familiarity with the chemical or energy industry, or prior experience in market research/analyst workflows.
- Exposure to frameworks such as OpenAI Agentic SDK, CrewAI, AutoGen, SmolAgent, etc.
- Experience deploying ML/LLM solutions to production environments (Docker, CI/CD).
- Hands-on experience with vector databases such as FAISS, Weaviate, Pinecone, or ChromaDB.
- Experience with dashboarding tools and visualization libraries (e.g., Streamlit, Plotly, Dash, or Tableau).
- Exposure to cloud platforms (AWS, GCP, or Azure), including usage of GPU instances and model hosting services.
Key Skills
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