Key Responsibilities
- 1. Lead AI Research & Model Development
- Lead AI research and development, designing and implementing state-of-the-art machine learning and deep learning models.
- Develop cutting-edge AI algorithms for NLP, computer vision, generative AI, and bias detection.
- Optimize deep learning architectures (transformers, CNNs, RNNs, diffusion models) for accuracy, efficiency, and cost-effective inference.
- Apply advanced mathematical concepts (linear algebra, probability, discrete mathematics, statistics, optimization) to AI model development.
- 2. Build & Deploy GenAI Solutions
- Build and deploy Python-based GenAI solutions for analyzing and improving ad content.
- Work with large-scale, complex datasets — from acquisition to preprocessing, feature engineering, and augmentation — to enable high-quality model training and evaluation.
- Design and implement LLM-based pipelines using tools like LangChain, LlamaIndex, and OpenAI APIs.
- Own development of RAG systems, vector search, and prompt workflows.
- 3. Production-Ready AI Systems
- Build robust APIs to integrate AI models into production systems (FastAPI, Flask, Django).
- Design scalable AI solutions with MLOps best practices for production-ready deployment.
- Move fast: ship features, not just prototypes.
Required Skills & Experience
- 1. Core AI/ML Expertise
- 5+ years of experience in Python and AI/ML engineering.
- Strong understanding of LLM-based workflows, embeddings, prompt engineering, and RAG.
- Experience with modern GenAI frameworks (e.g., LangChain, LlamaIndex).
- Experience working with large-scale datasets, preprocessing, feature engineering, and augmentation.
- Familiarity with vector databases (e.g., FAISS, Pinecone, Weaviate).
- 2. Software Engineering & Deployment
- Ability to build and deploy APIs using FastAPI, Flask, or equivalent.
- Solid software engineering habits: clean code, version control, CI/CD.
- Cloud computing expertise (AWS, GCP, Azure) for scalable AI deployments.
- Experience with LLM cost optimization and open-source model deployment.
- 3. Leadership & Collaboration
- Mentoring and guiding AI engineers, fostering a research-driven development culture.
Why Join us?
- Work on large-scale AI projects with real-world impact
- High autonomy, high-impact role on a lean, ambitious team
- Shape both the product and technical foundation of AI systems
- Fully remote work with flexibility
- Competitive compensation and opportunities for career growth.
- Continuous learning, experimentation, and exposure to cutting-edge AI tools
Originally posted on Himalayas
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