Role Overview
We are looking for a Machine Learning Developer to design and build scalable AI systems.
This role goes beyond traditional model development. You will work on:
● core machine learning and deep learning systems
● LLM-based applications and knowledge pipelines
● retrieval and reasoning systems (RAG)
● productionization of AI models and services
You will help translate complex data and scientific problems into robust, production-grade AI systems.
What You’ll Work On
● Designing and developing machine learning and deep learning models
● Building scalable data pipelines for training, evaluation, and inference
● Help in developing and productionizing AI systems as APIs and services
● Designing and implementing RAG pipelines for knowledge-driven applications
● Working with LLM frameworks such as LangChain and LlamaIndex
● Building embedding pipelines and integrating vector search systems
● Optimizing model performance, latency, and scalability
● Collaborating with backend teams to integrate AI systems into products
Tech Stack
- Core ML: PyTorch, TensorFlow, Scikit-learn
- Data: NumPy, Pandas
- LLM / RAG: LangChain, LlamaIndex, vector databases, embeddings
- Backend Integration: FastAPI, Django (for model serving)
- Cloud: AWS (primary), Azure, GCP
- Other: REST APIs, async processing, Docker
What We’re Looking For
● Strong proficiency in Python and machine learning libraries
● Solid understanding of machine learning and deep learning fundamentals
● Experience building and deploying ML models in production environments
● Experience with data preprocessing, feature engineering, and model evaluation
Systems & AI Engineering
● Experience in productionizing ML systems (model APIs, pipelines, inference systems)
● Understanding of scalable ML architectures and data pipelines
● Familiarity with handling large datasets and compute-intensive workloads
● Experience integrating ML models into real-world applications
Modern AI Stack (Important)
● Experience with LangChain, LlamaIndex, or similar LLM frameworks
● Understanding of RAG (Retrieval-Augmented Generation) pipelines
● Experience with embeddings, semantic search, and vector databases
● Familiarity with prompt design and LLM-based application workflows
Nice to Have
● Experience with generative models, graph-based models, or diffusion models
● Exposure to life sciences, cheminformatics, or scientific data
● Experience with Docker, Kubernetes, and deployment pipelines
● Experience working on AI-first or data platform products
