Junior AI Engineer

Working Hours : Full-time
Locations : Hyderabad Experience : Min 2 years apply now apply now About The Role Soothsayer Analytics is a global AI & Data Science consultancy headquartered in Detroit, with a thriving delivery center in Hyderabad We design and deploy end-to-end custom Machine Learning & GenAI solutions?spanning predictive analytics, optimization, NLP, computer vision, and enterprisescale AI platforms?that help leading enterprises forecast, automate, and gain a competitive edge This is a unique opportunity to begin your AI career by working on cutting-edge GenAI, ML, and datadriven projects across industries Job Overview We seek a Junior AI Engineer with min 2years of hands-on experience (or strong internships/projects) in machine learning, NLP, or GenAI You will support the development and deployment of AI models, contribute to data preparation pipelines, and learn best practices in MLOps and applied AI engineering Key Responsibilities Model Development & Support Assist in building ML models (classification, regression, clustering, forecasting) Contribute to fine-tuning and prompt engineering for LLMs (GPT, LLaMA, etc) Experiment with vector databases and RAG pipelines for GenAI applications Data Preparation & Engineering Work with structured/unstructured datasets for ML training Perform data cleaning, feature engineering, and basic pipeline development MLOps & Deployment (Learning Role) Support containerization and deployment of models using Docker/Kubernetes Learn to implement CI/CD pipelines for ML workflows Collaboration & Learning Work under guidance of senior engineers and data scientists Document experiments and present results to technical and business stakeholders Required Skills & Qualifications Education: Bachelors in Computer Science, AI, Data Science, or related field Experience: 12 years (including internships, academic projects, or full-time roles) Technical Skills Programming: Python (pandas, NumPy, scikit-learn, PyTorch/TensorFlow) ML Basics: Regression, classification, clustering, time-series GenAI/LLMs: Prompt engineering, LangChain basics, RAG concepts Databases: SQL (basic), exposure to NoSQL/vector DBs (Pinecone, FAISS) MLOps Exposure: Docker, Git, basic CI/CD knowledge Bonus: Knowledge of vision models, transformers, or cloud AI services (AWS Sagemaker, Azure ML, GCP Vertex AI) Skills Matrix Candidates must submit a detailed resume and fill out the following matrix: Skill Details Skills Last Used Experience (months) Self-Rating (010) Python (pandas, sklearn, PyTorch/TensorFlow) ML Basics (Regression, Clustering, etc) GenAI/LLMs (Prompting, LangChain, etc) SQL / NoSQL Vector Databases (FAISS, Pinecone, pgvector) MLOps Tools (Docker, Git, CI/CD) Cloud AI Platforms (AWS/Azure/GCP) Instructions For Candidates Provide a detailed resume highlighting academic/industry projects in ML and AI Fill out the above skills matrix with accurate details and self-ratings