AI/ML Engineer – Data Science & Data Engineering
Python
Kubernetes
Docker
Data Science
Machine Learning (ML)
Deep LearningKey Responsibilities
- Design, build, and optimize scalable data pipelines for AI/ML applications.
- Develop, train, evaluate, and deploy Machine Learning and Deep Learning models.
- Build production-ready LLM applications using Retrieval-Augmented Generation (RAG), prompt engineering, and vector databases.
- Fine-tune open-source and foundation models using domain-specific datasets.
- Develop and maintain end-to-end MLOps pipelines for model deployment, monitoring, and lifecycle management.
- Perform data preprocessing, feature engineering, exploratory data analysis (EDA), and model evaluation.
- Develop APIs and AI services for production deployment.
- Collaborate with cross-functional teams to deliver scalable AI-driven solutions.
- Monitor model performance, troubleshoot production issues, and maintain technical documentation.
Required Skills
Mandatory
- 1–3 years of experience in Data Science, Data Engineering, or AI/ML development.
- Strong programming skills in Python and SQL.
- Hands-on experience with Machine Learning frameworks such as PyTorch, TensorFlow, or Scikit-learn.
- Experience building LLM-powered applications using RAG, Prompt Engineering, and Embeddings.
- Hands-on experience with LangChain, LlamaIndex, CrewAI, or n8n for LLM orchestration and AI workflow automation.
- Experience in LLM fine-tuning and working with Hugging Face models.
- Knowledge of MLOps concepts including model deployment, monitoring, versioning, and CI/CD.
- Experience with Git, REST APIs, Linux environments, and data processing libraries.
Preferred
- Experience with vector databases such as Pinecone, Chroma, Milvus, or Weaviate.
- Familiarity with Docker, Kubernetes, and MLflow.
- Exposure to Apache Spark or Airflow for data engineering workflows.
- Experience with cloud platforms (AWS, Azure, or GCP).
Primary Technology Stack
- Languages & Data Processing: Python, SQL, Pandas, NumPy, Apache Spark
- AI & Machine Learning: PyTorch, TensorFlow, Scikit-learn
- Application Frameworks: LangChain, LlamaIndex, CrewAI, n8n
- Core Methodologies: Retrieval-Augmented Generation (RAG), Model Fine-Tuning, Prompt Engineering, Embeddings
- Models & Infrastructure: OpenAI APIs, Hugging Face Ecosystem, Embedding Models
- Vector Databases: Pinecone, Chroma, Milvus, Weaviate
- Databases: PostgreSQL, MongoDB
- MLOps & DevOps: Docker, Kubernetes, MLflow, CI/CD, Git
- Cloud Platforms: AWS, Azure, GCP
Experience: 1–3 Years
Domain: Data Science | Data Engineering | Machine Learning | Generative AI | MLOps
Shubham Vishwakarma
Full Stack Developer - Averlon
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