Description

Responsibilities

AI/ML Model Development

Design, develop, and optimize machine learning and deep learning models

Build NLP, computer vision, or predictive analytics solutions

Train, test, and evaluate models for accuracy, scalability, and performance

Fine-tune pre-trained models (e.g., LLMs, transformers) for business use cases

Data Engineering & Processing (Optional)

Collect, clean, and preprocess structured and unstructured datasets

Work with large-scale data pipelines and streaming data systems

Implement feature engineering and data transformation workflows

Deployment & MLOps

Deploy models into production using APIs, containers, or microservices

Work with DevOps to build CI/CD pipelines for ML workflows (MLOps)

Monitor model performance, drift, and reliability in production

Optimize latency, throughput, and cost efficiency

Cloud & System Integration

Integrate AI solutions into cloud platforms (AWS)

Work with services like SageMaker, Azure ML, Vertex AI, or OpenAI APIs

Collaborate with DevOps, backend, and frontend teams for implementation

Research & Innovation

Stay up to date with emerging AI technologies and frameworks

Evaluate and implement GenAI, LLMs, and prompt engineering techniques

Prototype and experiment with new AI-driven solutions

Skills

Must have

Technical Skills

Programming: Python (preferred), Java, or Scala

ML Frameworks: TensorFlow, PyTorch, Scikit-learn

AI/GenAI: prompt engineering (preferred), Claude CLI / Code(preferred), LLMs(preferred) Hugging Face, OpenAI APIs

Data Tools (Optional): Pandas, NumPy, Spark

APIs & Microservices development

Version control (Git)

Cloud & DevOps

Experience with AWS

Containers: Docker, ECS/EKS

CI/CD pipelines (GitHub Actions Or Jenkins Or GitLab CI)

Data & Systems

Databases: SQL, NoSQL

Familiarity with data pipelines and ETL processes (Optional)

Understanding of distributed systems

Key Skills

Education

Bachelor's degree