🔍 AI Engineer — phablo.ai (Remote) Location: Remote Type: Full-time Company: Phablo.ai Team: Founding Engineering Team Experience: 1–4 years (exceptional freshers welcome) 🌟 About Us At Phablo.ai , we’re transforming how compliance works for life sciences. No more clunky tools or manual processes — we’re building an AI-powered platform that helps teams stay compliant faster, smarter, and with confidence. Co-Founded by domain and tech experts from Germany & India, headquartered in SIngapore , our mission is global: to reimagine compliance workflows for the world’s most regulated industries. 🚀 Perks & Culture 🧠 Founding Opportunity – Join as part of our early core engineering team and shape the product and tech stack from the ground up. 🌍 Remote-First, Global Team – Collaborate with co-founders and teammates across continents . Our key markets are the EU and US. 💰 Compensation – Competitive startup salary with strong growth upside. As the company grows, so will your salary and other perks. Equity opportunities available for long-term, high-impact contributors at later stages. 📚 Limitless Learning – Work on cutting-edge LLM, RAG, and document AI systems with real-world impact. ⚙️ Culture of Innovation – We move fast, experiment often, and value initiative, autonomy, and team spirit. 🎯 Responsibilities Architect and implement components of our AI compliance engine using techniques like RAG, hybrid search, summarization, and entity extraction. Fine-tune and optimize foundation models (e.g., LLaMA, Mistral, GPT) for tasks such as regulation parsing, document comparison, and compliance Q&A. Build document ingestion, chunking, and embedding pipelines using LangChain , Haystack , or LlamaIndex . Integrate with vector databases (e.g., FAISS, Qdrant, Weaviate, Pinecone) to enable scalable semantic retrieval. Develop and deploy FastAPI/Flask-based APIs for real-time or batch AI inference services. Apply prompt engineering, memory techniques, and few-shot learning to improve response quality, accuracy, and explainability. Work closely with domain experts and product teams to build AI systems aligned with real regulatory workflows . Continuously monitor and evaluate AI model performance across metrics like latency, accuracy, hallucination rate, and compliance risk. 🛠️ Required Skills Strong Python skills and experience with ML frameworks like PyTorch , TensorFlow , or JAX . Deep understanding of LLMs and NLP workflows including: NER, summarization, semantic similarity, RAG, and hybrid search Embedding generation and vector search optimization Familiar with tools like: LangChain , Haystack , LlamaIndex Vector DBs : FAISS, Weaviate, Qdrant, Pinecone FastAPI , Flask , or similar frameworks for serving models Hands-on with MLOps tooling like MLflow, Weights & Biases, HuggingFace Hub, or cloud platforms (AWS, GCP, Vertex AI). Awareness of AI safety , data privacy , and security best practices in regulated industries. Excellent problem-solving, communication, and collaboration skills. Bonus: Experience in life sciences , healthcare , legal tech , or regulatory AI is highly desirable. 🎓 Qualifications 1–4 years of experience engineering AI systems in production settings. OR, if you’re a fresher : A strong academic background in AI/ML/NLP/Data Science from a reputed institution. Demonstrated ability through research publications , open-source contributions , or top performance in AI competitions. 💬 Let’s Build the Future of Compliance If you're excited about building AI systems that solve real-world problems in healthcare, pharma, and life sciences — let’s talk! Apply now or reach out directly to ts@phablo.ai Show more Show less
