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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: Bachelor’s in Computer Science, AI, Data Science, or related field.

Experience: 1–2 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:

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.