Working Hours : Full-time

Locations : Hyderabad

Experience : Min 2 years

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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 enterprise-scale 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 data-driven projects across industries.

Job Overview

We seek a Junior AI Engineer with minimum 2 years of hands-on experience (or strong internships or 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 such as classification, regression, clustering, and forecasting.
Contribute to fine-tuning and prompt engineering for LLMs such as GPT and LLaMA.
Experiment with vector databases and RAG pipelines for GenAI applications.
Data Preparation & Engineering
Work with structured and 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 and Kubernetes.
Learn to implement CI/CD pipelines for ML workflows.
Collaboration & Learning
Work under the 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.
Programming: Python (pandas, NumPy, scikit-learn, PyTorch or TensorFlow)
ML Basics: Regression, classification, clustering, time-series
GenAI / LLMs: Prompt engineering, LangChain basics, RAG concepts
Databases: SQL (basic), exposure to NoSQL and vector databases (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

Skill Details Last Used Experience (Months) Self-Rating (0–10) 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 or industry projects in machine learning and AI.
Fill out the above skills matrix with accurate details and self-ratings.