Senior AI Scientist (ML + Data Engineering Focus)

Working Hours : Full Time Locations : Hyderabad Experience : 6 –10 years apply now apply now About The Role Soothsayer Analytics is a global AI and Data Science consultancy headquartered in Detroit, with a thriving delivery center in Hyderabad. We design and deploy end-to-end custom Machine Learning solutions spanning predictive analytics, optimization, NLP, and GenAI that help leading enterprises forecast, automate, and gain a competitive edge. Join us to tackle high-impact, cross-industry projects where your ideas move rapidly from concept to production, shaping the future of data-driven decision-making. We seek a Senior AI Scientist with strong ML fundamentals and data engineering expertise to lead the development of scalable AI/LLM solutions. You will design, fine-tune, and deploy models (e.g., LLMs, RAG architectures) while ensuring robust data pipelines and MLOps practices. Key Responsibilities AI/LLM Development: Fine-tune and optimize LLMs (e.g., GPT, Llama) and traditional ML models for production. Implement retrieval-augmented generation (RAG), vector databases, and orchestration tools (e.g., LangChain). Data Engineering: Build scalable data pipelines for unstructured/text data (e.g., Spark, Kafka, Airflow). Optimize storage/retrieval for embeddings (e.g., pgvector, Pinecone). MLOps & Deployment: Containerize models (Docker) and deploy on cloud (AWS/Azure/GCP) using Kubernetes. Design CI/CD pipelines for LLM workflows (experiment tracking, monitoring). Collaboration: Work with DevOps to optimize latency/cost trade-offs for LLM APIs. Mentor junior team members on ML engineering best practices. Required Skills & Qualifications Education: MS/PhD in CS/AI/Data Science (or equivalent experience). Experience: 6+ years in ML + data engineering, with 2+ years in LLM/GenAI projects. Skills Matrix Candidates must submit a detailed resume and fill out the following matrix: Skill Details Skills Last Used Experience (months) Self-Rating (0–10) Python ML SQL/NoSQL Apache Spark/Kafka LLM Frameworks (LangChain, etc.) MLOps (Docker/K8s) Cloud (AWS/Azure/GCP) Vector Databases (Pinecone, pgvector) Instructions For Candidates Provide a detailed resume highlighting projects related to LLMs, data engineering, and MLOps. Fill out the matrix above with accurate dates, experience duration, and self-ratings.