Description

Key Responsibilities

• Design, develop, train, and deploy machine learning and deep learning models.
• Develop scalable AI/ML pipelines for large datasets and production environments.
• Apply supervised, unsupervised, and reinforcement learning techniques.
• Build and optimize NLP, Generative AI, LLM, and conversational AI solutions.
• Work with LLMs, RAG, embeddings, vector databases, prompt engineering, and fine-tuning.
• Develop AI applications using frameworks such as PyTorch, TensorFlow, Scikit-learn, Hugging Face, LangChain, and LlamaIndex.
• Perform feature engineering, model selection, hyperparameter tuning, and model evaluation.
• Build and maintain MLOps pipelines for model training, deployment, monitoring, and retraining.
• Deploy ML models using Docker, Kubernetes, CI/CD, MLflow, and cloud platforms.
• Work with AWS, Azure, or GCP AI/ML services.
• Process and analyze large datasets using SQL, Spark, PySpark, Pandas, and NumPy.
• Implement model monitoring, logging, governance, security, and performance optimization.
• Collaborate with Data Engineers, Software Engineers, Data Scientists, Product Managers, and business stakeholders.
• Mentor junior AI/ML engineers and provide technical leadership.

Required Skills

Programming

• Python
• SQL
• Java / Scala – good to have
• Pandas
• NumPy

Machine Learning

• Machine Learning
• Deep Learning
• Scikit-learn
• Regression
• Classification
• Clustering
• Recommendation Systems
• Time Series
• Feature Engineering
• Model Optimization

AI / Deep Learning

• TensorFlow
• PyTorch
• Keras
• CNN
• RNN
• LSTM
• Transformers

Generative AI

• Generative AI
• LLM
• OpenAI / Azure OpenAI
• Hugging Face
• LangChain
• LlamaIndex
• RAG
• Vector Databases
• Embeddings
• Prompt Engineering
• Fine-tuning
• AI Agents

MLOps / DevOps

• MLflow
• Kubeflow
• Docker
• Kubernetes
• CI/CD
• Git
• Jenkins / GitHub Actions
• Model Deployment
• Model Monitoring

Cloud

• AWS / Azure / GCP
• S3
• SageMaker
• Azure ML
• Vertex AI
• Databricks

Databases / Big Data

• SQL
• PostgreSQL
• MongoDB
• Snowflake
• Databricks
• Apache Spark
• PySpark
• Vector DB – Pinecone / FAISS / Weaviate / Azure AI Search

Preferred Qualifications

• Bachelor's or Master's degree in Computer Science, AI, Machine Learning, Data Science, Engineering, or related field.
• 8+ years of experience in software engineering, data science, AI/ML, or related areas.
• 3+ years of hands-on experience building and deploying production ML models.
• Strong experience with cloud-based AI/ML solutions.
• Experience leading AI/ML projects from architecture through production deployment.
• Strong communication, problem-solving, and technical leadership skills

Key Skills

Education

Bachelor's or Master's degrees