Highlights
Hands-on ML & GenAI projects, cutting-edge technologies, global consultancy firm.
Description
Job Summary
pJoin Soothsayer Analytics, a leading global AI & Data Science consultancy with a thriving delivery center in Hyderabad. As a Junior AI Engineer, you will support the development and deployment of cutting-edge ML and GenAI solutions across industries. This role offers hands-on experience in model building, data preparation, MLOps, and collaboration with senior engineers.
Responsibilities
- 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.
- Work with structured/unstructured datasets for ML training.
- Perform data cleaning, feature engineering, and basic pipeline development.
- Support containerization and deployment of models using Docker/Kubernetes.
Required Skills
- Data Cleaning
- ML Model Development
- MLOps Knowledge
- Prompt Engineering
- Feature Engineering
Required Skills Explained
- Python: Proficiency with libraries such as pandas, NumPy, scikit-learn, PyTorch, and TensorFlow is essential for developing and deploying AI models.
- ML Basics: Understanding of core concepts including regression, classification, clustering, and time-series analysis to build robust predictive models.
- GenAI/LLMs: Knowledge in prompt engineering and basics of LangChain as well as RAG (Retrieval-Augmented Generation) for building conversational AI applications.
- Databases: Familiarity with SQL and exposure to NoSQL or vector databases like Pinecone, FAISS is necessary for handling structured and unstructured data efficiently.
- MLOps Tools: Experience with containerization using Docker and Kubernetes, as well as basic CI/CD pipelines for managing ML workflows.
- Cloud AI Platforms: Bonus points for knowledge of cloud services such as AWS SageMaker, Azure Machine Learning, or GCP Vertex AI to integrate these tools into your projects.
Who is this for
pSeeking candidates with a passion for AI and experience in machine learning, NLP, or GenAI. Ideal for individuals looking to gain hands-on experience and learn best practices in MLOps and applied AI engineering.
Why This Job is a Good Opportunity
ulliWork on cutting-edge GenAI and ML projects with leading enterprises.liJoin a global consultancy that values innovation and creativity in AI solutions.
liGrowth potential within the fast-growing field of data science and machine learning.liCollaboration with experienced engineers and access to mentorship programs.
Interview Preparation Tips
- Review your resume and skills matrix thoroughly to be ready for detailed discussions about past projects and experiences.
- Prepare examples of how you have applied machine learning techniques in real-world scenarios or academic projects.
- Be familiar with the latest trends in GenAI, such as LLMs and RAG pipelines, and discuss your understanding of these technologies.
- Discuss specific MLOps tools like Docker and CI/CD that you are comfortable with and any experience deploying models in production environments.
- Prioritize clear communication and presentation skills to explain technical concepts effectively during the interview.
Career Growth in This Role
pThe role of a Junior AI Engineer at Soothsayer Analytics provides a solid foundation for career growth in data science and machine learning. With experience in model development, data preparation, and MLOps practices, you can transition into more specialized roles such as Senior AI Engineer or Machine Learning Scientist.pAdditionally, the opportunity to work on diverse projects across industries allows you to expand your skill set and become a versatile data scientist capable of tackling complex problems. The company's global presence also opens doors for international career opportunities in different regions.
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Skills
Frequently Asked Questions
What are the key responsibilities of this role?You will assist in building ML models, contribute to fine-tuning and prompt engineering for LLMs, and support MLOps tasks.
What technical skills are required for this position?Candidates should have proficiency in Python (pandas, NumPy), machine learning basics, GenAI/LLMs, data cleaning, and basic pipeline development.
How much experience is needed to apply?Applicants must have 1-2 years of hands-on experience or strong internships/projects in relevant fields.
